research objectives translate

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Research Translation Toolkit

POSTED September 27, 2021

Research Translation

Choose a file to download:

• Stakeholder Analysis
• Communication Products
• Troubleshooting Guide
• Toolkit Factsheet

Research translation is the process that transforms research findings into a form that is relevant to practitioners or other audiences. This toolkit supports researchers, helping them with research translation by providing exercises, fillable forms, and templates as well as links to examples and key resources. The short video linked above briefly outlines the rationale behind developing this research translation toolkit. A factsheet that provides a quick snapshot of the toolkit, including the purpose and value of each section and when to use it in the research process, is also available for download from the dropdown menu above.

Each toolkit section guides researchers through a series of steps to prepare specific outputs. Section one, Communication Products , focuses on how to write a factsheet or policy brief. Section two, Stakeholder Analysis , focuses on identifying, prioritizing, and tracking engagement with stakeholders that can be influential in using research to impact programs, policy, and practice in a sector. Section three, Research-to-Action (R2A) Plan , guides readers through the development of a plan designed to accomplish research translation goal(s) and objectives. 

Each section may be used independently.  Each section folder includes a guide, accompanying worksheets and examples, a “Readme” file with detailed instructions, and a troubleshooting guide.

Communication Products Section

Stakeholder Analysis Section

Research-to-Action (R2A) Plan Section

Research Translation Toolkit Webinar Series

RTAC held a four-part webinar series that provided an overview of our Research Translation Toolkit and a deep-dive into each of its three sections. The webinar series highlighted what the toolkit is, why it’s important to your work, when you can use it in the research process and how you can use it yourself. The slides, recording, and additional materials for each webinar are available on the webinar series page .

Additional Toolkit Resources

Additional resources to support use of the Research Translation Toolkit can be found in the panel to the right. The first video linked introduces the toolkit and briefly outlines the rationale behind its development. The second and third videos are testimonials of research teams who developed research-to-action plans and communication products to advance the application of their research. The second video shares the experience of researchers at Indian Agricultural Research Institute and Michigan State University on off-grid, clean energy cooling for affordable storage of perishables for bottom-of-the-pyramid farmers. In the third video, researchers from Kenyatta University share how they worked to enhance efficient utilization of soil moisture for improved smallholder farm productive in the semi-arid areas of Kenya. The final video demonstrates how users can access the toolkit and files on the RTAC website.

A factsheet that provides a quick snapshot of the toolkit, including the purpose and value of each section and when to use it in the research process, is also available in the side panel and for download from the dropdown menu above.

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• Open access
• Published: 31 May 2012

Knowledge translation of research findings

• Jeremy M Grimshaw 1 ,
• Martin P Eccles 2 ,
• John N Lavis 3 , 6 ,
• Sophie J Hill 4 &
• Janet E Squires 5  

Implementation Science volume  7 , Article number:  50 ( 2012 ) Cite this article

181k Accesses

1343 Citations

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One of the most consistent findings from clinical and health services research is the failure to translate research into practice and policy. As a result of these evidence-practice and policy gaps, patients fail to benefit optimally from advances in healthcare and are exposed to unnecessary risks of iatrogenic harms, and healthcare systems are exposed to unnecessary expenditure resulting in significant opportunity costs. Over the last decade, there has been increasing international policy and research attention on how to reduce the evidence-practice and policy gap. In this paper, we summarise the current concepts and evidence to guide knowledge translation activities, defined as T2 research (the translation of new clinical knowledge into improved health). We structure the article around five key questions: what should be transferred; to whom should research knowledge be transferred; by whom should research knowledge be transferred; how should research knowledge be transferred; and, with what effect should research knowledge be transferred?

We suggest that the basic unit of knowledge translation should usually be up-to-date systematic reviews or other syntheses of research findings. Knowledge translators need to identify the key messages for different target audiences and to fashion these in language and knowledge translation products that are easily assimilated by different audiences. The relative importance of knowledge translation to different target audiences will vary by the type of research and appropriate endpoints of knowledge translation may vary across different stakeholder groups. There are a large number of planned knowledge translation models, derived from different disciplinary, contextual ( i.e. , setting), and target audience viewpoints. Most of these suggest that planned knowledge translation for healthcare professionals and consumers is more likely to be successful if the choice of knowledge translation strategy is informed by an assessment of the likely barriers and facilitators. Although our evidence on the likely effectiveness of different strategies to overcome specific barriers remains incomplete, there is a range of informative systematic reviews of interventions aimed at healthcare professionals and consumers ( i.e. , patients, family members, and informal carers) and of factors important to research use by policy makers.

There is a substantial (if incomplete) evidence base to guide choice of knowledge translation activities targeting healthcare professionals and consumers. The evidence base on the effects of different knowledge translation approaches targeting healthcare policy makers and senior managers is much weaker but there are a profusion of innovative approaches that warrant further evaluation.

Peer Review reports

Globally we spend billions of dollars each year in both the public and private sectors on biomedical, clinical, and health services research, undergraduate healthcare professional training and continuing professional development, quality improvement, patient safety, and risk management. Despite this, healthcare systems fail to ensure that effective and cost-effective programs, services, and drugs get to all of those who need them; and healthcare professionals fail to provide the level of care to which they aspire. One of the most consistent findings from clinical and health services research is the failure to translate research into practice and policy. For example, McGlynn and colleagues observed that patients in the USA received 55% of recommended care, and that quality varied by medical condition ranging from 79% of recommended care for senile cataract to 11% of recommended care for alcohol dependence [ 1 ]. Similar findings have been reported globally in both developed and developing settings, in both primary care and specialty-provided care and in care provided by all disciplines [ 2 ]. As a result of these evidence-practice gaps, patients fail to benefit optimally from advances in healthcare resulting in poorer quality of life and loss of productivity both personally and at the societal level.

In addition to the limited use of effective treatments, there is also evidence that around 20% to 30% of patients may get care that is not needed or care that could be potentially harmful [ 3 ]. Because of these evidence-practice gaps, patients are exposed to unnecessary risks of iatrogenic harms and healthcare systems are exposed to unnecessary expenditure resulting in significant opportunity costs.

Over the last 10 to 15 years, there has been increasing international policy and research attention on how to reduce the evidence-practice and policy gap. Across different healthcare systems, different terms describe these efforts including quality assurance, quality improvement, knowledge translation, knowledge utilisation, knowledge transfer and exchange, innovation diffusion, implementation research, research utilisation, evidence-informed policy, and evidence-informed health systems [ 4 , 5 ]. These different terms often cover related and overlapping constructs. Commenting on the terminology of quality assurance in 1982, Donabedian noted that ‘we have used these words in so many different ways that we no longer clearly understand each other when we say them’ [ 6 ]. Throughout this paper, we use the term ‘knowledge translation’ which has gained currency in Canada and globally over the last decade. There are two main types of translational research. T1 research refers to the translation of basic biomedical research into clinical science and knowledge, while T2 research refers to the translation of this new clinical science and knowledge into improved health [ 7 ]. In this paper, we refer to T2 research. We define knowledge translation as ‘ensuring that stakeholders are aware of and use research evidence to inform their health and healthcare decision-making.’ This definition recognizes that there are a wide range of stakeholders or target audiences for knowledge translation, including policy makers, professionals (practitioners), consumers ( i.e. , patients, family members, and informal carers), researchers, and industry.

While knowledge translation is a relatively new term, the notion of moving research findings into practice is not new. It can be traced back to the investigations of French sociologist Gabriel Tarde at the beginning of the 20th century who attempted to explain why some innovations are adopted and spread throughout a society, while others are ignored [ 8 ]. The current conceptualization of knowledge translation evolved out of several diverse disciplinary perspectives, including knowledge utilisation, diffusion of innovations, technology transfer, evidence-based medicine, and quality improvement [ 9 ]. Interest in knowledge translation has increased dramatically in recent years due to recognition that traditional approaches to moving research into practice, which were predominantly based on education ( e.g. , continuing professional development CPD), did not lead to optimal care. In this paper, based on a previously published monograph chapter [ 10 ], we summarise the current concepts and evidence to guide knowledge translation activities. We structure the article around five key questions identified by Lavis and colleagues [ 11 ]:

What should be transferred?

To whom should research knowledge be transferred, by whom should research knowledge be transferred, how should research knowledge be transferred, with what effect should research knowledge be transferred.

The increased focus on knowledge translation has frequently emphasised individual studies as the unit for knowledge translation. While this may be appropriate when the targets for knowledge translation are other researchers or research funders (who need to be aware of primary research results), we argue that this is inappropriate when the targets for knowledge translation are consumers, healthcare professionals, and/or policy makers. This is because individual studies rarely, by themselves, provide sufficient evidence for practice and policy changes. In fact, individual studies may be misleading due to bias in their conduct or random variations in their findings, although some exceptionally large randomised trials may be sufficiently persuasive by themselves to warrant practice or policy change, e.g. , the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) [ 12 ] and the International Study of Infarct Survival 2 (ISIS-2) Trial [ 13 ].

Ioannidis and colleagues undertook a series of landmark studies of research exploring the evolution of evidence in healthcare (summarized in [ 14 ]). In both basic and clinical sciences, they observed the ‘Proteus phenomenon’—that the first published study on a scientific question may find the most extravagant effect size and that as further evidence is gathered, effect sizes tend to diminish [ 14 ]. They observed that thousands of observations were required before estimates of gene disease association became stable [ 15 ]. They also noted that the results of even highly cited clinical research studies published in major medical and specialty journals were likely to be contraindicated or found to be exaggerated with further accumulation of evidence [ 16 ]. As a result, Ioannidis and colleagues argued that replication and evidence synthesis is needed before knowledge translation [ 14 ].

We suggest that the results of individual studies need to be interpreted within the context of global evidence before deciding whether it is ready for knowledge translation. In other words, the basic unit of knowledge translation should be up-to-date systematic reviews or other syntheses of the global evidence. Greater emphasis on the results of systematic reviews would increase the ‘signal to noise’ of knowledge translation activities and may increase the likelihood of their success. Over the last twenty years, healthcare research funders and healthcare systems have made considerable investments in knowledge syntheses, especially those targeting the needs of healthcare practitioners and patients. Examples include the substantial number of publicly funded national guideline and health technology programs, The Cochrane Collaboration [ 17 ], and the US funded Evidence-based Practice Centers [ 18 ].

The question ‘What should be transferred?’ also challenges knowledge translators to identify the key messages for different target audiences and to fashion these in language and knowledge translation products that are easily assimilated by different audiences. Over the past decade, a variety of different products have been developed targeting different audiences (for example, decision aids for patients [ 19 ], clinical practice guidelines for healthcare professionals [ 20 ], and actionable messages [ 11 ] and policy briefs [ 21 ] for policy makers).

The relative importance of knowledge translation to different target audiences will vary by the type of research being translated. For example, primary target audiences for knowledge translation of the results of basic science include other researchers and industry; whereas primary target audiences for knowledge translation of the results of population health research include other researchers, administrators, and policy makers (See Table  1 ).

The relative importance of different target audiences will also vary by the results of the research [ 22 ]. For example, the primary target audiences for clinical research demonstrating lack of benefit or harms from a drug sufficient to warrant its withdrawal might be national policy makers (including regulatory bodies) and industry. Whereas, the primary target audiences for clinical research demonstrating benefits from a drug to suggest its widespread use might be patients, healthcare practitioners, local administrators as well as national policy makers, and industry (See Table  2 ).

The messenger in knowledge translation efforts may be an individual ( e.g. , healthcare practitioner, researcher, or consumer), group, organization, or even healthcare system. The most appropriate messenger will vary according to the target audience and research knowledge being transferred. Shonkoff suggests that in determining ‘who’ should be the messenger credibility is important [ 23 ]. Research supports this view; Hayward and colleagues found that an authoritative endorsement by a respected physician organization or physician colleague influenced physicians’ use of clinical practice guidelines in practice [ 24 ]. With public policy makers, Lavis and colleagues suggest that the most credible messengers might include organizations of government officials [ 11 ].

Building credibility and acting as a messenger for the transfer of research knowledge is a time-consuming and skill-intensive process, making it impossible to use a ‘one size fits all’ approach to deciding ‘by whom should research knowledge be transferred’ [ 11 ]. Researchers typically carry the responsibility for conducting knowledge translation. They should, however, only be the messenger when they have credibility with the target audience, possess the skills and experience needed to transfer the research knowledge at hand, and have time and resources to do so. A more appropriate approach to effective and sustainable knowledge translation may be the development of research knowledge infrastructures by healthcare systems that address the needs of their various stakeholders ( e.g. , consumers, practitioners, managers, and policy makers). Ellen and colleagues define research knowledge infrastructure as any instrument ( i.e. , programs, tools, devices) implemented in a healthcare system in order to facilitate access, dissemination, exchange, and/or use of evidence [ 25 ]. Components of research knowledge infrastructures are classified into two broad categories: technological and organizational. Technological components include electronic databases and search engines. Organizational components include documentation specialists, data analysts, knowledge brokers ( i.e. , individuals who manage the collaboration between an organization, external information, and knowledge producers and users), and training programs (to assist with activities such as searching for information, quality appraisal, adaption and use of the research findings) [ 25 , 26 ].

In Canada, some knowledge translation researchers have invested significant time and financial resources into building technological (online databases with built-in search engines) resources that can be used by healthcare systems as part of a research knowledge infrastructure. Rx for Change is an online database that houses syntheses of the global evidence from systematic reviews: on the effectiveness of interventions for improving prescribing by healthcare professionals and medicines use by consumers; of professional interventions that impact the delivery of care; and of organizational, financial, and regulatory interventions that influence professional behaviour. The methods used to populate the database parallel systematic-review methodology. Rx for Change is publicly accessible and contains research information relevant to healthcare professionals, consumers, policy makers, and researchers [ 27 ].

Health Systems Evidence is also an online database, but primarily targets policy makers and senior managers (and other individuals responsible for assisting or making public policy decisions). Common criticisms of systematic reviews by policy makers include the absence of relevant reviews, and difficulty accessing and understanding reviews. Health Systems Evidence addresses these criticisms in order to facilitate the use of systematic reviews in health systems and policy decision making. Health Systems Evidence is a repository of syntheses of research evidence about governance, financial, and delivery arrangements within health systems, and about implementation strategies that can support change in health systems. The database contains policy briefs, overviews of systematic reviews, systematic reviews, and soon will contain a range of other types of documents needed in the policymaking process, such as economic evaluations.

Both databases ( Rx for Change and Health Systems Evidence ) provide improved access to research information for consumers, practitioners, and/or policy makers. However, this access is necessary but not sufficient to ensure knowledge translation. Effective and sustainable knowledge translation also requires organizational knowledge infrastructure components. Ellen and colleagues developed a research knowledge infrastructure framework that identified potential organizational components that a healthcare system could have in its research knowledge infrastructure. This framework is based on an environmental scan and scoping review of existing literature. The broad organizational domains included in the framework are: climate for research use, research production, activities used to link research to action including push efforts ( i.e. , efforts undertaken by researchers to disseminate research evidence to knowledge users), pull efforts ( i.e. , efforts by knowledge users to access and use research evidence), and exchange efforts ( i.e. , efforts focused on building and maintaining relationships between researchers and knowledge users), and evaluation of efforts [ 25 ]. This framework is currently being evaluated in a study examining knowledge-translation platforms in 41 countries [ 25 , 28 ].

Planning for knowledge translation

There are a large number of planned knowledge translation models, derived from different disciplinary and contextual viewpoints [ 29 , 30 ]. Most of these suggest that planned knowledge translation is more likely to be successful if an assessment of the likely barriers and facilitators informs the choice of knowledge translation strategy. In this section, we briefly discuss types of barriers, potential approaches for identifying barriers, and factors influencing the choice of knowledge translation intervention.

Identifying barriers to knowledge translation

Common barriers across target groups include issues relating to knowledge management, such as the sheer volume of research evidence currently produced, access to research evidence sources, time to read evidence sources and skills to appraise and understand research evidence. Over the past twenty years, there has been substantial investment by many healthcare systems to address these knowledge management barriers. For example, the conduct of systematic reviews and development of clinical practice guidelines to reduce the volume of research evidence and the time needed to read evidence sources; investment in electronic libraries of health and public access evidence sources to improve access to research evidence; and the development of critical appraisal skills tools and training to improve research literacy skills.

However while better knowledge management is necessary, it is unlikely by itself to be sufficient to ensure knowledge translation because of barriers working at different levels of healthcare systems, many of which operate at levels beyond the control of an individual practitioner. For example, barriers may operate at other levels of a healthcare system including: structural barriers ( e.g. financial disincentives), organizational barriers ( e.g. inappropriate skill mix, lack of facilities or equipment), peer group barriers ( e.g. local standards of care not in line with desired practice), professional ( e.g. knowledge, attitudes and skills) and professional-patient interaction barriers ( e.g. communication and information processing issues). Evidence in support of this can be found in a structured review of healthcare professionals’ views on engagement in quality improvement activities [ 31 ]. In this review, Davies and colleagues concluded that many of the barriers to participating in quality improvement activities identified by professionals arise from problems related to working effectively between and across health professions. This means that although knowledge management resources ( e.g. , more time and more resources) may be necessary and even helpful, they are unlikely to be sufficient to overcome the other ‘organizational’ barriers professionals face to engage in quality improvement (and knowledge translation) activities [ 31 ].

There are diverse methods for identifying potential barriers including qualitative approaches (individual interviews, focus groups), surveys and direct observation [ 32 ]. However, there are no standard approaches available yet. As a result, those involved with knowledge translation activities need to use their judgement about how best to elicit barriers given their understanding of the context and potential barriers and resources available to them.

Choosing interventions

Unfortunately, our evidence on the likely effectiveness of different strategies to overcome specific barriers to knowledge translation remains incomplete. Individuals involved in knowledge translation need to: identify modifiable and non-modifiable barriers relating to behavior; identify potential adopters and practice environments; and prioritise which barriers to target based upon consideration of ‘mission critical’ barriers. Furthermore, the potential for addressing these barriers through knowledge translation activities (based upon consideration of the likely mechanisms of action of interventions) and the resources available for knowledge translation activities also needs to be addressed.

Effectiveness of professional behaviour change strategies

The Cochrane Effective Practice and Organisation of Care (EPOC) group supports reviews of interventions to improve healthcare systems and healthcare delivery [ 33 ]. It has identified over 7,000 randomised and quasi-experimental studies and conducted 80 systematic reviews of professional, organisational, financial, and regulatory interventions within its scope by August 2011.

EPOC has prepared two overviews of systematic reviews and is currently updating these [ 34 , 35 ]. It has identified over 300 systematic reviews of professional behaviour change strategies. In this section, we summarise the results of key Cochrane EPOC reviews, selected because they are in general of higher quality and more up-to-date than non-Cochrane systematic reviews of similar focus [ 36 ]. We provide a definition of each intervention, the likely mechanism of action of the intervention, and any comments relating to the practical delivery of the intervention (including the resources required). The details and findings of the reviews of the interventions, including the median and range of effect sizes observed, are presented in Table  3 .

Generally, similar median absolute effect sizes are reported across the interventions. While one interpretation might be that the choice of intervention is less important than doing something/anything ( i.e. , that the observed effects are largely non specific (Hawthorne-like) effects), we do not believe this to be the case. The interquartile range of absolute effect sizes covers almost 30 percentage points and varies by intervention (see Table  3 ). Furthermore, the variation in observed effects within intervention category (for example the interquartile range of observed effects in trials of audit and feedback was +3% to +11% absolute improvements in performance) suggest that the effects of interventions vary presumably related to the degree to which the mechanism of action of the intervention addresses the underlying barriers in a study. The interventions also have very different mechanisms of action, and there is likely to be confounding within and across reviews. In other words, researchers are likely to have tested interventions that they believed likely effective given the particular behaviours and likely barriers within the context of their study. Finally, because we are reporting absolute effects some broad commonality of effect sizes is to be expected. In general, interventions are not tested in the expectation of producing large absolute effect sizes. Most cluster trials are powered to detect effects in the range of 10 to 20 percent absolute improvement. Under these circumstances similarity of observed effects is not surprising.

Printed educational materials

EPOC defines printed educational materials as the ‘distribution of published or printed recommendations for clinical care, including clinical practice guidelines, audio-visual materials and electronic publications. The materials may have been delivered personally or through mass mailings’ [ 37 ]. In general, printed educational materials target knowledge and potential skill gaps of individual healthcare professionals. While they could also be used to target motivation when written as a ‘persuasive communication’ there is little evidence of them being used in this way. Printed educational materials are commonly used, have a relatively low cost and are generally feasible in most settings.

Educational meetings

EPOC defines educational meetings as the ‘participation of healthcare providers in conferences, lectures, workshops or traineeships’ [ 38 ]. An important distinction is between didactic meetings (that largely target knowledge barriers at the individual healthcare professional/peer group level) and interactive workshops (that can target knowledge, attitudes, and skills at the individual healthcare professional/peer group level). Educational meetings are commonly used, with the main cost related to the release time for healthcare professionals, and are generally feasible in most settings.

Educational outreach

EPOC defines educational outreach or academic detailing as ‘use of a trained person who meets with providers in their practice settings to give information with the intent of changing the providers’ practice. The information given may have included feedback on the performance of the provider(s)’ [ 39 ]. Soumerai and Avorn suggest that educational outreach derives from social marketing approaches that target an individual’s knowledge and attitudes [ 44 ]. Typically, the detailer aims to get a maximum of three messages across during a 10 to 15 minute meeting with a healthcare provider. The detailer will tailor their approach to the characteristics of the individual healthcare provider, and typically use additional provider behaviour change strategies to reinforce their message. Most studies of educational outreach have focused on changing relatively simple behaviours in the control of individual physician behaviors such as the choice of drugs to prescribe.

Educational outreach programs have been used across a wide range of healthcare settings especially to target prescribing behaviours. They require considerable resources including the costs of detailers and preparation of materials. Nevertheless, Mason and colleagues observed that educational outreach may still be efficient to change prescribing patterns [ 45 ].

Local opinion leaders

EPOC defines local opinion leaders as ‘use of providers nominated by their colleagues as ‘educationally influential’ [ 40 ]. The investigators must have explicitly stated that their colleagues identified the opinion leaders.’ Opinion leadership is the degree to which an individual is able to influence other individuals’ attitudes or overt behaviour informally in a desired way with relative frequency. This informal leadership is not a function of the individual’s formal position or status in the system; it is earned and maintained by the individual’s technical competence, social accessibility, and conformity to the systems norms. When compared to their peers, opinion leaders have greater exposure to all forms of external communication, have somewhat higher social status and are more innovative. However, the most striking feature of opinion leaders is their unique and influential position in their system’s communication structure; they are at the centre of interpersonal communication networks (interconnected individuals who are linked by patterned flows of information). Opinion leaders target the knowledge, attitudes, and social norms of their peer group. The potential success of opinion leaders is dependent upon the existence of intact social networks within professional communities. Grimshaw and colleagues observed that the existence of such networks varied across communities and settings within the UK [ 46 ]. They also observed that opinion leaders were condition-specific; in other words, colleagues identified different opinion leaders for different clinical problems. Doumit also observed that opinion leaders where not stable over time [ 47 ]. The resources required for opinion leaders include costs of the identification method, training of opinion leaders and additional service costs.

Audit and feedback

EPOC defines audit and feedback as ‘any summary of clinical performance of healthcare over a specified period of time’ to change health professional behaviour, as indexed by ‘objectively measured professional practice in a healthcare setting or healthcare outcomes.’ The summary may also have included recommendations for clinical action. The information may have been obtained from medical records, computerised databases, or observations from patients. The subsequent feedback of and resulting action planning based on the audit summary are also important elements of an audit and feedback intervention [ 41 , 48 ]. Adams and colleagues observed that healthcare professionals often over estimated their performance by around 20% to 30% [ 49 ]. Audit and feedback target healthcare provider/peer groups’ perceptions of current performance levels and is useful to create cognitive dissonance within healthcare professionals as a stimulus for behaviour change. The resources required to deliver audit and feedback include data abstraction and analysis costs and dissemination costs. The feasibility of audit and feedback may depend on the availability of meaningful routine administrative data for feedback.

EPOC defines reminders as ‘patient or encounter specific information, provided verbally, on paper or on a computer screen, which is designed or intended to prompt a health professional to recall information [ 42 ]. This would usually be encountered through their general education, in the medical records or through interactions with peers, and so remind them to perform or avoid some action to aid individual patient care. Computer aided decision support and drugs dosage are included.’ Reminders prompt healthcare professionals to remember to do important items during professional-patient interactions [ 50 ]. The majority of early studies on computerized reminders were undertaken in highly computerized US academic health science centres, and their generalisability to other settings is less certain [ 51 ]. The resources required vary across the delivery mechanism. Additionally, there is insufficient knowledge at present about how to prioritise and optimize reminders.

Tailored interventions

Tailored interventions are ‘strategies to improve professional practice that are planned taking account of prospectively identified barriers to change’ [ 43 ]. Barriers to change refer to factors that have the potential to impair the effectiveness of interventions designed to improve professional behaviour/practice. EPOC classifies barriers to change into nine categories (information management, clinical uncertainty, sense of competence, perceptions of liability, patient expectations, standards of practice, financial disincentives, administrative constraints, and other) [ 52 ]. In a recent review, Baker and colleagues assessed the effectiveness of interventions tailored to address identified barriers to change on professional practice or patient outcomes and found that tailored interventions are more likely to improve professional practice ( e.g. , prescribing and adherence to guideline recommendations) than is no intervention or the dissemination of guidelines or educational materials. Further research is needed to determine the effectiveness of tailored interventions in comparison with other interventions [ 43 ].

Multifaceted interventions

EPOC defines multifaceted interventions as ‘any intervention including two or more components.’ Multifaceted interventions potentially target different barriers in the system. Grimshaw and colleagues explored whether there was a dose response curve for multifaceted interventions and observed that effect sizes did not necessarily increase with increasing number of components (Figure  1 ) [ 20 ]. They also observed that few studies provided any explicit rationale or theoretical base for the choice of intervention. As a result, it was unclear whether researchers had an a priori rationale for the choice of components in multifaceted interventions based upon possible causal mechanisms or whether a ‘kitchen sink’ approach formed the basis for the choice. It is plausible that multifaceted interventions built upon a careful assessment of barriers and coherent theoretical base may be more effective than single interventions. Multifaceted interventions are likely to be more costly than single interventions. When planning multifaceted interventions, it is important to carefully consider how components are likely to interact to maximise benefits.

Effect sizes of multifaceted interventions by number of interventions.

Effectiveness of knowledge translation strategies focusing on consumers

The Cochrane Consumers and Communication Review Group supports systematic reviews of the effects of interventions (particularly those which focus on information and communication) which affect consumers’ interactions with healthcare professionals, healthcare services and healthcare researchers [ 53 ]. Outcomes of interest include effects on people’s knowledge and decision-making, healthcare use, experience of healthcare, and health and wellbeing. They have identified over 7,000 randomised studies and conducted 35 systematic reviews of interventions and one overview of systematic reviews [ 54 ] within their scope to August 2011.

The Cochrane Consumers and Communication Review Group have developed a taxonomy for organising interventions. Categories relevant to knowledge translation include interventions: to facilitate communication and/or decision making; to support behaviour change; and to inform and educate. In this section, we summarize the range of intervention types relevant to knowledge translation by consumers. Drawing from the Cochrane reviews, we present the authors’ definition of each intervention; the details and findings of the reviews are presented in Table  4 .

Interventions to facilitate communication and/or decision-making

Three interventions to facilitate communication and/or decision making that have been the focus of Cochrane systematic reviews are decision aids, personalised risk communication, and communication before consultations. Decision aids are a type of decision support intervention designed to help people make choices about health treatment options. Stacey (following O’Connor, who prepared the first Cochrane review), defines them as interventions containing ‘detailed, specific, and personalized information to help people focus on options and outcomes for the purpose of decision making’ [ 55 ]. They are important for decisions where there is uncertainty about a specific course of action. Personalised risk communication refers to the provision of information to consumers that is personally relevant to them. It is sometimes used to present and discuss the risks and benefits of healthcare in general, and of screening in particular, to consumers. As Edwards and colleagues outline, it can be based on a consumer’s own risk factors for a condition ( e.g. , their age) or calculated from their risk factors using epidemiological formulas. In the latter, the information is often presented as an absolute risk or as a risk score, or categorised into, for example, high-, medium-, or low-risk groups. Personalised risk communication may also be less detailed, for example, a listing of a consumer’s risk factors to guide discussion and intervention [ 56 ]. In their Cochrane review, Kinnersley and colleagues operationalise communication before consultations to include any intervention delivered before consultations, and which has been designed to help consumers (and/or their representatives) address their information needs within consultations [ 57 ].

Interventions to support behaviour change

One area that continues to challenge the Cochrane Consumers and Communication Review Group is the identification of effective interventions that support behaviour change. Four interventions which have been the focus of Cochrane reviews in this area are: interactive health communication applications; interventions to enhance medication adherence; contracts; and new methods of communication. Interactive health communication applications, defined by Murray and colleagues, are computer-based (usually web-based) information packages for patients that combine health information with at least one of: social support, decision support, or behaviour change support [ 58 ]. Interventions to enhance medication adherence include a wide range of single and multifaceted interventions; Haynes and colleagues identified: instruction, counseling, automated telephone monitoring and counseling, manual telephone follow-up, family intervention, increasing the convenience of care, simplified dosing, self-monitoring, reminders, special ‘reminder’ pill packaging, dose-dispensing units and medication charts, appointment and prescription refill reminders, reinforcement/rewards, medication formulations, crisis intervention, direct observation of treatments, lay health mentoring, comprehensive pharmaceutical care services, and psychological therapy in their Cochrane review [ 60 ]. Contracts refer to formalised (written or verbal) mutual agreements between two or more parties [ 61 ]. New methods of communication to date have included communicating DNA-based disease risk estimates to change health behaviours on lifestyle ( e.g. , smoking, physical activity, diet) [ 62 ] and providing consumers with a visual presentation ( i.e. , the source images) of their medical imaging ( i.e. , of magnetic resonance imaging, tomography, radiography, and/or ultrasonography) results to increase consumers’ engagement in health-related behaviours [ 63 ].

Interventions to inform and educate

Two interventions which have been the focus of Cochrane reviews to ‘inform and educate’ consumers are written information and self-management programmes. Written information is one of the most ubiquitous interventions targeting consumers [ 64 ].

Self management programmes have become a major initiative of government and community organizations in the area of chronic illness [ 65 ]. They promote various strategies for people to take an active approach to managing their health.

Effectiveness of knowledge translation strategies focusing on policy makers and senior health service managers

In contrast to the substantial evidence base on the effectiveness of knowledge translation strategies targeting healthcare professionals and consumers, few systematic reviews exist of interventions evaluating the effects of knowledge translation strategies for policy makers or senior health service managers. One review, conducted by Perrier and colleagues, evaluated interventions to increase the use of systematic reviews by health policy makers and managers [ 66 ]. Two studies were included in the review. The first study utilized a non-experimental design to report an intervention where public health policy makers were offered the opportunity to receive five relevant reviews. At three months and two years, respectively, 23% and 63% of respondents reported using at least one of the systematic reviews to make a policy decision. The second study was a randomised trial where health departments received one of three interventions: access to an online registry of systematic reviews, tailored messages plus access to the online registry of systematic reviews, or tailored messages plus access to the registry along with a knowledge broker who worked one-on-one with decision makers over a period of one year. While none of the interventions showed a significant effect on global evidence-informed decision making, tailored messages plus access to the online registry of systematic reviews showed a positive significant effect on public health policies and programs [ 66 ].

Lavis and colleagues conducted a systematic review of factors that influence the use of research evidence in public policy making [ 67 ]. Five criteria were used to assess validity of the included studies: the use of two or more data collection methods; a random or purposive sampling strategy; response rate >60%; two or more types of research use are examined; and two or more competing variables are examined.

A total of 16 studies met the criteria of using two or more data collection methods. These studies were conducted across a variety of jurisdictions, policy domains, content areas, and time periods. There was relatively little consistency in findings. However, two factors emerged with some frequency as being important to policy makers’ use of research evidence: interactions between researchers and policy makers in the context of policy networks such as formal advisory committees and in the context of informal relationships; and research that matched the beliefs, values, interests, or political goals and strategies of elected officials, social interest groups, and others. Both factors increased the prospects for research use by policy makers [ 67 ].

The findings from these reviews and other findings have led to the development of a number of knowledge translation approaches targeting policy makers and senior health services managers [ 28 , 68 , 69 ]. For example, a series of tools called SUPPORT Tools for evidence-informed health policy making (STP) were developed to assist policy makers in using research evidence. These tools were developed by members of the SUPporting POlicy relevant Reviews and Trials (SUPPORT) project, an international collaboration funded by the European Commission’s 6th Framework [ 70 ] ( http://www.supportcollaboration.org ). The SUPPORT tools describe a series of processes to help ensure that relevant research is identified, appraised and used appropriately by policy makers. The tools address four broad areas of interest related to policymaking: supporting evidence-informed policymaking [ 71 – 73 ]; identifying needs for research evidence in relation to clarifying problems, framing options, and planning implementation [ 74 – 76 ]; finding and assessing evidence from systematic reviews [ 77 – 79 ] and other kinds of evidence [ 80 , 81 ]; and moving from research evidence to decisions. The focus in this final area is on engaging stakeholders in evidence-informed policymaking [ 21 , 82 , 83 ] and on addressing how to use research evidence in decisions [ 84 – 86 ]. By focusing on how to ‘support’ the use of research evidence in health policymaking, the SUPPORT tools should increase the use of research evidence by policy makers [ 87 ] .

The SUPPORT tools describe a variety of packaging and push, facilitating pull, and exchange activities. Packaging and push activities focus on the activities of researchers to disseminate their research findings to a broad audience above and beyond traditional routes of dissemination such as publication in peer reviewed journals [ 11 ]. Examples of packaging and push activities include: increased emphasis on knowledge syntheses as the unit for knowledge translation; actionable messages; graded entry formats to allow the research user to access the level of detail that he or she requires (for example, the Canadian Health Services Research Foundation requires research reports to have one page of main messages, a three-page executive summary, and then no more than 25 pages for the complete project); using multiple communication channels tailored to the target audience; targeted electronic push of information relevant to the specific needs of research users—examples include the Contacts, Help, Advice and Information Network (C.H.A.I.N.) ([ 88 ], http://chain.ulcc.ac.uk/chain/ accessed 5 July 2011) and E-watch bulletin on Innovation in Health Services ( http://www.ohpe.ca/node/2740 accessed 5 July 2011); workshops and seminars with target audiences; and development of tools to help research users apply research findings in their own settings.

Facilitating pull activities focus on the needs of users, and creating an appetite for research results [ 11 ]. Pull activities include various training activities to improve policy makers’ and senior managers’ research literacy and interest. For example, the Canadian Health Services Research Foundation provides the EXTRA program to train senior healthcare executives in research application ( http://www.chsrf.ca/Programs/EXTRA.aspx accessed 5 July 2011). ‘One stop’ initiatives such as Health Systems Evidence also facilitate pull.

Exchange activities focus on building and maintaining new relationships between researchers and policy makers and senior managers to exchange knowledge and ideas [ 69 , 89 ]. For example, several research-funding programs require active participation of decision makers (sometimes including co-funding by healthcare organisations) in research teams. The rationale is that decision makers are more likely to consider research findings if they are actively involved in the research conducted in their settings to answer specific contextualized questions. These approaches legitimately focus on local knowledge translation of individual studies. However, the results of these studies should still be incorporated into systematic reviews to judge whether additional knowledge translation activities should be undertaken outside the context and relationships of the original study. Other exchange approaches include deliberative dialogues and the use of knowledge brokers to act as ‘human intermediaries’ between the world of research and action [ 69 , 82 , 90 ].

This profusion of approaches to improving knowledge translation to policy makers and senior healthcare managers highlights the increased recognition of the failure of traditional diffusion approaches to knowledge translation for this target group ( e.g. , [ 90 ]). Most of these approaches have a strong theoretical basis and face validity. However, it will be important to evaluate their benefits, harms and costs fully.

Appropriate endpoints of knowledge translation may vary across different stakeholder groups. For example, knowledge translation targeting policy makers and consumers should ensure that consideration of research evidence is a key component of their decision making, but recognize that there are other legitimate factors (for example, the policy context for policy makers, values and preferences of individual patients) that need to considered [ 91 – 93 ]. Thus, the resulting decision is likely to be evidence-informed but may not be particularly evidence-based. However, knowledge translation targeting professionals should result in practice that is more evidence-based and is likely to be observable as reflected in changes in professional behaviours and quality indicators.

In this paper, we have attempted to briefly summarise some of the key concepts and evidence about the effectiveness of knowledge translation activities targeting different stakeholder groups. We particularly recommend the five key questions developed by Lavis and colleagues as an aide for researchers and others involved in knowledge translation when developing knowledge translation activities [ 11 ]. There is a substantial (if incomplete) evidence base to guide choice of knowledge translation activities targeting healthcare professionals and patients. The evidence base on the effects of different knowledge translation approaches targeting healthcare policy makers and senior managers is much weaker but there are a profusion of innovative approaches that warrant further evaluation.

Grol observed that many current knowledge translation activities are based on participants’ beliefs, rather than evidence about the likely effectiveness of different approaches [ 94 ]. Grol challenged healthcare systems to develop and use a robust evidence base to support the choice of knowledge translation strategies, arguing, ‘evidence-based medicine should be complemented by evidence-based implementation.’ While we are some way from achieving this goal, there are grounds for optimism. Over the past twenty-five years, healthcare systems have invested heavily in knowledge synthesis activities that facilitate timely access of evidence. Further, it is possible to achieve clinically important practice changes by healthcare professionals and improved patient decision making with current knowledge translation activities.

McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA: The quality of health care delivered to adults in the United States. N Engl J Med. 2003, 348: 2635-2645. 10.1056/NEJMsa022615.

Article   PubMed   Google Scholar  

Grol R: Successes and failures in the implementation of evidence-based guidelines for clinical practice. Med Care. 2001, 39: II46-II54.

Article   CAS   PubMed   Google Scholar  

Schuster MA, McGlynn EA, Brook RH: How good is the quality of health care in the United States? 1998. Milbank Q. 2005, 83: 843-895. 10.1111/j.1468-0009.2005.00403.x.

Article   PubMed   PubMed Central   Google Scholar  

McKibbon KA, Lokker C, Wilczynski NL, Ciliska D, Dobbins M, Davis DA, Haynes RB, Straus SE: A cross-sectional study of the number and frequency of terms used to refer to knowledge translation in a body of health literature in 2006: a Tower of Babel?. Implement Sci. 2010, 5: 16-10.1186/1748-5908-5-16.

Tetroe JM, Graham ID, Foy R, Robinson N, Eccles MP, Wensing M, Durieux P, Legare F, Nielson CP, Adily A, Ward JE, Porter C, Shea B, Grimshaw JM: Health research funding agencies' support and promotion of knowledge translation: an international study. Milbank Q. 2008, 86: 125-155. 10.1111/j.1468-0009.2007.00515.x.

Donabedian A: Explorations in quality assessment and monitoring: II - the criteria and standards of quality. 1982, Health Administration Press, Ann Arbor, Michigan

Google Scholar  

Sung NS, Crowley WF, Genel M, Salber P, Sandy L, Sherwood L, Johnson S, Catanese V, Tilson H, Getz K, Larson E, Scheinberg D, Reece E, Slavkin H, Dobs A, Grebb J, Martinez R, Korn A, Rimoin D: Central challenges facing national clinical research enterprise. JAMA. 2003, 289: 1278-1287. 10.1001/jama.289.10.1278.

Tarde G: The law of imitiation. 1903, HOLT, New York

Estabrooks CA, Derksen L, Winther C, Lavis JN, Scott SD, Wallin L, Profetto-McGrath J: The intellectual structure and substance of the knowledge utilization field: A longitudinal author co-citation analysis, 1945 to 2004. Implement Sci. 2008, 3: 49-10.1186/1748-5908-3-49.

Grimshaw J, Eccles MP: Knowledge Translation Of Research Findings. Effective Dissemination of Findings from Research. Edited by: Jonson E. 2008, Institute of Health Economics, Edmonton

Lavis JN, Robertson D, Woodside JM, McLeod CB, Abelson J: How can research organizations more effectively transfer research knowledge to decision makers?. Milbank Q. 2003, 81: 221-222. 10.1111/1468-0009.t01-1-00052.

ALLHAT Collaborative Research Group: Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium chanel blocker vs diuretic. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). JAMA. 2002, 288: 2981-2997. 10.1001/jama.288.23.2981.

Article   Google Scholar  

International Study of Infarct Survival Collaborative Group: Randomised trial of intravenous streptokinase, oral asprin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2 (Second International Study of Infarct Survival) Collaborative Group. Lancet. 1988, 2: 349-360.

Ioannidis JP: Evolution and translation of research findings: from bench to where?. PLoS Clin Trials. 2006, 1: e36-10.1371/journal.pctr.0010036.

Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG: Replication validity of genetic association studies. Nat Genet. 2001, 29: 306-309. 10.1038/ng749.

Ioannidis JP: Contradicted and initially stronger effects in highly cited clinical research. JAMA. 2005, 294: 218-228. 10.1001/jama.294.2.218.

Grimshaw JM, Santesso N, Cumpston M, Mayhew A, McGowan J: Knowledge for knowledge translation: the role of the Cochrane Collaboration. J Contin Educ Health Prof. 2006, 26: 55-62. 10.1002/chp.51.

Helfand M, Morton SC, Guallar E, Mulrow C: Challenges of Summarizing Better Information for Better Health: The Evidence-based Practice Center Experience. Ann Intern Med. 2005, 142: 1033-1126.

O'Connor AM, Bennett C, Stacey D, Barry MJ, Col NF, Eden KB, Entwistle V, Fiset V, Holmes-Rovner M, Khangura S, Llewellyn-Thomas H, Rovner D: Do patient decision aids meet effectiveness criteria of the International Patient Decision Aid Standards Collaboration? A systematic review and meta-analysis. Med Decis Making. 2007, 27:

Grimshaw JM, Thomas RE, Maclennan G, Fraser C, Ramsay CR, Vale L, Whitty P, Eccles MP, Matowe L, Shirran L, Wensing M, Dijkstra R, Donaldson C: Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess. 2004, 8: iii-72-

Lavis JN, Permanand G, Oxman AD, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 13: Preparing and using policy briefs to support evidence-informed policymaking. Health Res Policy Syst. 2009, 7 (Suppl 1): S13-10.1186/1478-4505-7-S1-S13.

Mowatt G, Thomson MA, Grimshaw J, Grant A: Implementing early warning messages on emerging health technologies. Int J Technol Assess Health Care. 1998, 14: 663-670. 10.1017/S0266462300011971.

Shonkoff JP: Science, policy, and practice: Three cultures in search of a shared mission. Child Development. 2000, 71: 181-187. 10.1111/1467-8624.00132.

Hayward RSA, Guyatt G, Moore KA, McKibbon KA, Carter AO: Canadian physicians' attitudes about and preferences regarding clinical practice guidelines. Canadian Medical Association Journal. 1997, 156: 1715-1723.

CAS   PubMed   PubMed Central   Google Scholar  

Ellen ME, Lavis J, Ouimet M, Grimshaw JM, Bedard PO: Determining research knowledge infrastructure for healthcare systems: A qualitative study. Implement Sci. 2011, 6:

Meso P, Smith R: A resource-based view of organizational knowledge management systems. J Knowledge Management. 2000, 4: 224-234. 10.1108/13673270010350020.

Weir M, Ryan R, Mayhew A, Worswick J, Santesso N, Lowe D, Leslie B, Stevens A, Hill S, Grimshaw JM: The Rx for Change database: A first-in-class tool for optimal prescribing and medicines use. Implement Sci. 2010, 5:

Lavis JN, Lomas J, Hamid M, Sewankambo NK: Assessing country-level efforts to link research to action. Bull World Health Organ. 2006, 84: 620-628. 10.2471/BLT.06.030312.

Grol R, Grimshaw J: From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003, 362: 1225-1230. 10.1016/S0140-6736(03)14546-1.

Graham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, Robinson N: Lost in knowledge translation: time for a map?. J Contin Educ Health Prof. 2006, 26: 13-24. 10.1002/chp.47.

Davies H, Powell A, Rushmer R: Healthcare professionals' views on clinician engagement in quality improvement: A literature review. Health Foundation. 2007

Grol R, Wensing M, Eccles MP: Improving patient care: implementing change in clinical practice. 2004, Elsevier, Oxford

Ballini L, Bero L, Eccles MP, Grimshaw J, Gruen RL, Lewin S: Effective Practice and Organisation of Care Group. The Cochrane Library About The Cochrane Collaboration (Cochrane Review Groups (CRGs). 2011

Bero LA, Grilli R, Grimshaw JM, Harvey E, Oxman AD, Thomson MA: Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. The Cochrane Effective Practice and Organization of Care Review Group. BMJ. 1998, 317: 465-468. 10.1136/bmj.317.7156.465.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Grimshaw JM, Shirran L, Thomas R, Mowatt G, Fraser C, Bero L, Grilli R, Harvey E, Oxman A, O'Brien MA: Changing provider behavior: an overview of systematic reviews of interventions. Med Care. 2001, 39: II2-45.

Moher D, Tetzlaff J, Tricco AC, Sampson M, Altman DG: Epidemiology and reporting characteristics of systematic reviews. PLoS Med. 2007, 4: e78-10.1371/journal.pmed.0040078.

Farmer AP, Legare F, Turcot L, Grimshaw J, Harvey E, McGowan JL, Wolf F: Printed educational materials: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2011, CD004398-

Forsetlund L, Bjorndal A, Rashidian A, Jamtvedt G, O'Brien MA, Wolf F, Davis D, Odgaard-Jensen J, Oxman AD: Continuing education meetings and workshops: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2009, CD003030-

O'Brien MA, Rogers S, Jamtvedt G, Oxman A, Odgaard-Jensen J, Kristoffersen D, Forsetlund L, Bainbridge D, Freemantle N, Davis DA, Haynes R, Harvey E: Educational outreach visits: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2008, CD000409-

Flodgren G, Parmelli E, Doumit G, Gattellari M, O'Brien MA, Grimshaw J, Eccles MP: Local opinion leaders: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2010, CD000125-

Jamtvedt G, Young JM, Kristoffersen DT, O'Brien MA, Oxman AD: Audit and feedback: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2010, CD000259-

Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J: The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev. 2011, CD001096-

Baker R, Camosso-Stefanovic J, Gilliss CL, Shaw EJ, Cheater F, Flottorp S, Robertson N: Tailored interventions to overcome identified barriers to change: Effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2010, CD005470-

Soumerai SB, Avorn J: Principles of educational outreach ('academic detailing') to improve clinical decision making. JAMA. 1990, 263: 549-556. 10.1001/jama.1990.03440040088034.

Mason J, Freemantle N, Nazareth I, Eccles M, Haines A, Drummond M: When is it cost-effective to change the behavior of health professionals?. JAMA. 2001, 286: 2988-2992. 10.1001/jama.286.23.2988.

Grimshaw JM, Eccles MP, Greener J, Maclennan G, Ibbotson T, Kahan JP, Sullivan F: Is the involvement of opinion leaders in the implementation of research findings a feasible strategy?. Implement Sci. 2006, 1: 3-10.1186/1748-5908-1-3.

Doumit G: Opinion leaders: Effectiveness, Identification, Stability, Specificity, and Mechanism of Action. 2006, PhD Thesis. University Of Ottawa

Gardner B, Whittington C, McAteer J, Eccles M, Michie S: Using theory to synthesize evidence from behaviour change interventions: The example of audit and feedback. Social Science & Medicine. 2010, 70: 1618-1625. 10.1016/j.socscimed.2010.01.039.

Adams AS, Soumerai SB, Lomas J, Ross-Degnan D: Evidence of self-report bias in assessing adherence to guidelines. Int J Qual Health Care. 1999, 11: 187-192. 10.1093/intqhc/11.3.187.

McDonald C: Protocol-based computer reminders, the quality of care and the non-prefectability of man. N Engl J Med. 1976, 295: 1351-1355. 10.1056/NEJM197612092952405.

Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG: Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006, 144: 742-752.

Cochrane Effective Practice and Organisation of Care group: Data collection checklist. EPOC measures for review authors. 2002

Prictor M, Hill S, Car J, Edwards A, Glenton C: Horey D et al. 2010, Cochrane Consumers and Communications Group, About The Cochrane Collaboration (Cochrane Review Groups (CRGs))

Ryan R, Santesso N, Hill S, Lowe D, Kaufman C, Grimshaw JM: Consumer-oriented interventions for evidence-based prescribing and medicines use: An overview of systematic reviews. Cochrane Database Syst Rev. 2011

Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Legare F, Thomson R: Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011

Edwards AG, Evans R, Dundon J, Haigh S, Hood K, Elwyn GJ: Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2006, CD001865-

Kinnersley P, Edwards A, Hood K, Cadbury N, Ryan R, Prout H, Owen D, Macbeth F, Butow P, Butler C: Interventions before consultations for helping patients address their information needs. Cochrane Database Syst Rev. 2007, CD004565-

Murray E, Burns J, See TS, Lai R, Nazareth I: Interactive Health Communication Applications for people with chronic disease. Cochrane Database Syst Rev. 2005, CD004274-

Bailey JV, Murray E, Rait G, Mercer CH, Morris RW, Peacock R, Cassell J, Nazareth I: Interactive computer-based interventions for sexual health promotion. Cochrane Database Syst Rev. 2010, CD006483-

Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X: Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008, CD000011-

Bosch-Capblanch X, Abba K, Prictor M, Garner P: Contracts between patients and healthcare practitioners for improving patients' adherence to treatment, prevention and health promotion activities. Cochrane Database Syst Rev. 2009

Marteau TM, French DP, Griffin SJ, Prevost AT, Sutton S, Watkinson C, Attwood S, Hollonds GJ: Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev. 2010

Hollands GJ, Hankins M, Marteau TM: Visual feedback of individuals' medical imaging results for changing health behaviour. Cochrane Database Syst Rev. 2010

Nicolson D, Knapp P, Raynor DK, Spoor P: Written information about individual medicines for consumers. Cochrane Database Syst Rev. 2009

Foster G, Taylor SJ, Eldridge SE, Ramsay J, Griffiths CJ: Self-management education programmes by lay leaders for people with chronic conditions. Cochrane Database Syst Rev. 2007, CD005108-

Perrier L, Mrklas K, Lavis J, Straus S: Interventions encouraging the use of systematic reviews by health policymakers and managers: A systematic review. Implement Sci. 2011, 6:

Lavis J, Hammill AC, Gildiner A, McDonagh RJ, Wilson MG, Ross SE, Ouimet M, Stoddart GL: A systematic review of the factors that influence the use of research evidence by public policymakers: Report submitted to the Canadian Population Health Initiative. 2005, Hamilton, Canada

Lavis JN: Research, public policymaking, and knowledge-translation processes: Canadian efforts to build bridges. J Contin Educ Health Prof. 2006, 26: 37-45. 10.1002/chp.49.

Lomas J: The in-between world of knowledge brokering. BMJ. 2007, 334: 129-132. 10.1136/bmj.39038.593380.AE.

Lavis JN, Oxman AD, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP). Health Res Policy Syst. 2009, 7 (Suppl 1): I1-10.1186/1478-4505-7-S1-I1.

Oxman AD, Lavis JN, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 1: What is evidence-informed policymaking?. Health Res Policy Syst. 2009, 7 (Suppl 1): S1-10.1186/1478-4505-7-S1-S1.

Oxman AD, Vandvik PO, Lavis JN, Fretheim A, Lewin S: SUPPORT Tools for evidence-informed health Policymaking (STP) 2: Improving how your organisation supports the use of research evidence to inform policymaking. Health Res Policy Syst. 2009, 7 (Suppl 1): S2-10.1186/1478-4505-7-S1-S2.

Lavis JN, Oxman AD, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 3: Setting priorities for supporting evidence-informed policymaking. Health Res Policy Syst. 2009, 7 (Suppl 1): S3-10.1186/1478-4505-7-S1-S3.

Lavis JN, Wilson MG, Oxman AD, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 4: Using research evidence to clarify a problem. Health Res Policy Syst. 2009, 7 (Suppl 1): S4-10.1186/1478-4505-7-S1-S4.

Lavis JN, Wilson MG, Oxman AD, Grimshaw J, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 5: Using research evidence to frame options to address a problem. Health Res Policy Syst. 2009, 7 (Suppl 1): S5-10.1186/1478-4505-7-S1-S5.

Fretheim A, Munabi-Babigumira S, Oxman AD, Lavis JN, Lewin S: SUPPORT Tools for Evidence-informed policymaking in health 6: Using research evidence to address how an option will be implemented. Health Res Policy Syst. 2009, 7 (Suppl 1): S6-10.1186/1478-4505-7-S1-S6.

Lavis JN, Oxman AD, Grimshaw J, Johansen M, Boyko JA, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 7: Finding systematic reviews. Health Res Policy Syst. 2009, 7 (Suppl 1): S7-10.1186/1478-4505-7-S1-S7.

Lewin S, Oxman AD, Lavis JN, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 8: Deciding how much confidence to place in a systematic review. Health Res Policy Syst. 2009, 7 (Suppl 1): S8-10.1186/1478-4505-7-S1-S8.

Lavis JN, Oxman AD, Souza NM, Lewin S, Gruen RL, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 9: Assessing the applicability of the findings of a systematic review. Health Res Policy Syst. 2009, 7 (Suppl 1): S9-10.1186/1478-4505-7-S1-S9.

Lewin S, Oxman AD, Lavis JN, Fretheim A, Marti SG, Munabi-Babigumira S: SUPPORT Tools for evidence-informed Policymaking in health 11: Finding and using evidence about local conditions. Health Res Policy Syst. 2009, 7 (Suppl 1): S11-10.1186/1478-4505-7-S1-S11.

Oxman AD, Fretheim A, Lavis JN, Lewin S: SUPPORT Tools for evidence-informed health Policymaking (STP) 12: Finding and using research evidence about resource use and costs. Health Res Policy Syst. 2009, 7 (Suppl 1): S12-10.1186/1478-4505-7-S1-S12.

Lavis JN, Boyko JA, Oxman AD, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 14: Organising and using policy dialogues to support evidence-informed policymaking. Health Res Policy Syst. 2009, 7 (Suppl 1): S14-10.1186/1478-4505-7-S1-S14.

Oxman AD, Lewin S, Lavis JN, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP) 15: Engaging the public in evidence-informed policymaking. Health Res Policy Syst. 2009, 7 (Suppl 1): S15-10.1186/1478-4505-7-S1-S15.

Oxman AD, Lavis JN, Fretheim A, Lewin S: SUPPORT Tools for evidence-informed health Policymaking (STP) 16: Using research evidence in balancing the pros and cons of policies. Health Res Policy Syst. 2009, 7 (Suppl 1): S16-10.1186/1478-4505-7-S1-S16.

Oxman AD, Lavis JN, Fretheim A, Lewin S: SUPPORT Tools for evidence-informed health Policymaking (STP) 17: Dealing with insufficient research evidence. Health Res Policy Syst. 2009, 7 (Suppl 1): S17-10.1186/1478-4505-7-S1-S17.

Fretheim A, Oxman AD, Lavis JN, Lewin S: SUPPORT Tools for Evidence-informed policymaking in health 18: Planning monitoring and evaluation of policies. Health Res Policy Syst. 2009, 7 (Suppl 1): S18-10.1186/1478-4505-7-S1-S18.

Lavis J, Oxman A, Lewin S, Fretheim A: SUPPORT Tools for evidence-informed health Policymaking (STP). Health Research Policy and Systems. 2009, 7: I1-10.1186/1478-4505-7-S1-I1.

Evans D, McAuley L, Santesso N, McGowan J, Grimshaw JM: Effective Health Care CHAIN (Contacts, Help, Advice and Information Network): Breaking Down Barriers between Professions, Organizations, Researchers and Practitioners in the UK and Canada. Innovations in Health Care: A Reality Check. Edited by: Casebeer AH, Mark AL. 2006, Palgrave Macmillan, Houndmills, 220-231.

Canadian Health Services Research Foundation: Issues in Linkage and Exchange between Researchers and Decision Makers. 1999

Lomas J: Improving research dissemination and uptake in the health sector: Beyond the sound of one hand clapping. 1997, ONT, Mcmaster University, Hamilton, 4-8-2011

Lavis J, Ross S, McLeod C, Glidiner A: Measuring the impact of health research. Journal of Health Services Research and Policy. 2003, 8: 165-170. 10.1258/135581903322029520.

Lavis JN, Ross SE, Hurley JE, Hohenadel JM, Stoddart GL, Woodward CA, Abelson J: Examining the role of health services research in public policymaking. Milbank Q. 2002, 80: 125-154. 10.1111/1468-0009.00005.

Squires J, Estabrooks C, O'Rourke H, Gustavsson P, Newburn-Cook C, Wallin L: A systematic review of the psychometric properties of self-report research utilization measures used in healthcare. Implement Sci. 2011, 6: 83-10.1186/1748-5908-6-83.

Grol R: Beliefs and evidence in changing clinical practice. British Medical Journal. 1997, 315: 418-421. 10.1136/bmj.315.7105.418.

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Acknowledgements

We are grateful to Egon Jonson for agreeing that we could use the chapter Grimshaw J, Eccles MP (2008). Knowledge Translation of Research Findings. In IHE Report June 2008: Effective Dissemination of Findings from Research. Institute of Health Economics, Alberta, Canada (2008) as the basis for this paper. JMG holds a Canada Research Chair. JES is a Postdoctoral Fellow funded by Canadian Institutes for Health Research. JMG and JNL are members and MPE is a member of the Scientific Advisory Board of KT Canada. Sophie Hill’s role as Coordinating Editor of Cochrane Consumers and Communication Review Group is supported by funding from the Quality, Safety and Patient Experience Branch, Department of Health Victoria, Australia and the National Health and Medical Research Council Funding for Australian-based Cochrane Collaboration Activities.

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Grimshaw, J.M., Eccles, M.P., Lavis, J.N. et al. Knowledge translation of research findings. Implementation Sci 7 , 50 (2012). https://doi.org/10.1186/1748-5908-7-50

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This chapter provides an overview of the research evaluating translation/interpreting learning and teaching practices. The chapter starts with highlighting the methodological paradigms and data sources commonly used in this research type. The author reviews and discusses the following six areas of translation/interpreting learning and teaching practices research: country-specific translator and interpreter education policies, training programme evaluation (i.e., evaluating one or more programmes, or a programme component), trainees’ needs analysis, trainees’ performance variables (i.e., personal, motivational and academic correlates), classroom practices (e.g., feedback provision and classroom motivation), and trainer education-related issues. At the end of the chapter, the author presents some suggestions for advancing translation/interpreting learning and teaching practices research.

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A. Niño Alonso. 2018. Exploring the use of Google Translate for independent language learning. Paper presented at the conference on Google Translate & Modern Languages Education, University of Nottingham, June 29 2018.

Google Scholar  

Afolabi, S. 2019. Translation and interpretation market needs analysis: towards optimizing professional translator and interpreter training in Nigeria. The Interpreter and Translator Trainer 13 (1): 104–106. https://doi.org/10.1080/1750399x.2019.1572997 .

Article   Google Scholar  

Alfayyadh, H. (2016). The feedback culture in translator education: A comparative exploration of two distinct university translation programs. Ph.D. dissertation, Kent State University, USA.

Álvarez-Álvarez, S., and V. Arnáiz-Uzquiza. 2017. Translation and interpreting graduates under construction: do Spanish translation and interpreting studies curricula answer the challenges of employability? The Interpreter and Translator Trainer 11 (2–3): 139–159. https://doi.org/10.1080/1750399x.2017.1344812 .

Araghian, R., and B. Ghonsooly. 2018. The relationship between burnout and personality. Babel 64 (5–6): 840–864. https://doi.org/10.1075/babel.00075.ara .

Ben Salamh, S.A. (2012). Second language literacy needs analysis of Saudi translation students at the college of languages. Ph.D. dissertation, Indiana University of Pennsylvania, USA.

Berwick, R. (1989). Needs Assessment in Language Programming: From Theory to Practice. In The second language curriculum , ed. R.K. Johnson, 48–62. Cambridge: Cambridge University Press.

Brindley, G. 1984. Needs analysis and objective setting in the Adult Migrant Education Program . Sydney: NSW Adult Migrant Education Service.

Brown, J.D. 1989. Language program evaluation: A synthesis of existing possibilities. In The second language curriculum , ed. R.K. Johnson, 222–241. Cambridge: Cambridge University Press.

Chapter   Google Scholar  

Chambers, F. 1980. A re-evaluation of needs analysis. ESP Journal 1 (1): 25–33.

Domínguez Araújo, L. 2019. Feedback in conference interpreter education: Perspectives of trainers and trainees. Interpreting 21 (1): 135–150. https://doi.org/10.1075/intp.00023.dom .

Dorado, C. and P. Orero. 2007. Teaching audiovisual translation online: A partial achievement. Perspectives: Studies in Translatology 15 (3): 191–202. https://doi.org/10.1080/13670050802153988 .

Eisner, E. (2002). The three curricula that all schools teach. In E. W. Eisner (Ed.),  The educational imagination: On the design and evaluation of school programs  (pp. 87–107). Upper Saddle River, NJ: Merrill Prentice Hall.

Enríquez Raído, V. 2018. Teacher motivation and emotions vis-à-vis students’ positive perceptions of effective teaching and learning: A self-case study of longitudinal data in reflective translation pedagogy. Translation, Cognition & Behavior 1 (2): 361–390. https://doi.org/10.1075/tcb.00016.enr .

Gabr, M. (2000). Reassessing translation programs in Egyptian national universities: Towards a model translation program. MA thesis, Washington International University, USA.

Gustafsson, K., E. Norström, and I. Fioretos. 2013. Community interpreter training in spoken languages in Sweden. International Journal of Interpreter Education 4 (2): 24–38.

Hale, S., and U. Ozolins. 2014. Monolingual short courses for language-specific accreditation: Can they work? A Sydney experience. The Interpreter and Translator Trainer 8 (2): 217–239. https://doi.org/10.1080/1750399x.2014.92937 .

Haro-Soler, M.M. (2017). Self-confidence and its role in translator training: The students’ perspective. In I. Lacruz and R. Jääskeläinen, Innovation and Expansion in Translation Process Research . ATA Series, John Benjamins.

Hubscher-Davidson, S. 2009. Personal diversity and diverse personalities in translation: A study of individual differences. Perspectives: Studies in Translatology 17 (3): 446–473. https://doi.org/10.1080/09076760903249380 .

Hutchinson, T., and A. Waters. 1987. English for specific purposes: A learning-centered approach . Cambridge: Cambridge University Press.

Book   Google Scholar  

Hutchinson, T., and A. Waters. 2002. English for specific purpose . Shanghai: Shanghai Foreign Language Education Press.

Hyland, K. 2006. English for academic purposes. An advanced resource book . London: Routledge.

Jaccomard, H. 2018. Work placements in Masters of translation: Five case studies from the University of Western Australia. Meta 63 (2): 532–547. https://doi.org/10.7202/1055151ar .

Khoshsaligheh, M., M. Moghaddas, and S. Ameri. 2019. English translator training curriculum revisited: Iranian trainees’ perspectives. Teaching English Language 13 (2): 181–212.

Kiely, R., and P. Rea-Dickins. 2005. Program evaluation in language education . Hampshire: PalgraveMacmillan.

Lee, Y.H. 2011. Comparing self-assessment and teacher’s assessment in interpreter training. T&I Review 1: 87–111.

Li, D. 2000. Needs assessment in translation teaching: Making translator training more responsive to social needs. Babel 46 (4): 289–299.

Li, D. 2002. Translator training: What translation students have to say. Meta 47 (4): 513–531. https://doi.org/10.7202/008034ar .

Li, D. 2007. Translation curriculum and pedagogy: Views of administrators of translation services. Target 19 (1): 5–33.

Li, D. 2012. Curriculum design, needs assessment and translation pedagogy, with special reference to translation training in Hong Kong . Beijing: Foreign Language Teaching and Research Press.

Li, X. 2019. Analyzing translation and interpreting textbooks: A pilot survey of business interpreting textbooks. Translation and Interpreting Studies 14 (3): 392–415. https://doi.org/10.1075/tis.19041.li .

Li, P., and Z. Lu. 2011. Learners’ needs analysis of a new optional college English course—Interpreting for non-English majors. Theory and Practice in Language Studies 1 (9): 1091–1102. https://doi.org/10.4304/tpls.1.9.1091-1102 .

Lim, K. (2006). A comparison of curricula of graduate schools of interpretation and translation in Korea. Meta: Translators’ Journal 51 (2): 215–228. https://doi.org/10.7202/013252ar .

Liu, C.F.M. 2017. Perception of translation graduates on translation internships, with mixed-methods approach. Babel 63 (4): 580–599.

Long, M. 2005. Second language needs analysis . Cambridge: Cambridge University Press.

Luo, J., and X. Ma. 2019. Reflection on consecutive interpreting note-taking textbooks published in China. International Journal of Applied Linguistics and Translation 5 (1): 9–14. https://doi.org/10.11648/j.ijalt.20190501.12 .

Lynch, B. 1996. Language program evaluation: Theory and practice . Cambridge: Cambridge University Press.

Mahasneh, A. (2013). Translation training in the Jordanian context: Curriculum evaluation in translator education. Ph.D. Dissertation, Binghamton University State University of New York, USA.

McDermid, D. 2009. The ontological beliefs and curriculum design of Canadian interpreter and ASL educators. International Journal of Interpreter Education 1: 7–32.

McDonough, J. 1984. ESP in perspective: A practical guide . London: Collins ELT.

Mo, Y., and S. Hale. 2014. Translation and interpreting education and training: Student voices. International Journal of Interpreter Education 6 (1): 19–34.

Munby, J. 1978. Communicative syllabus design . Cambridge: Cambridge University Press.

Mutlu, O. 2004. A needs analysis study for the English-Turkish translation course offered to management students of the Faculty of Economic and Administrative Sciences at Başkent University. M.A. thesis, Middle East Technical University, USA.

Nasrollahi Shahri, N., and Z. Barzakhi Farimani. 2016. A students’ needs-analysis for translation studies curriculum offered at master’s level in Iranian universities. Research in English Language Pedagogy 4 (1): 26–40.

Ordóñez-López, P. 2015. A critical account of the concept of ‘basic legal knowledge’: Theory and practice. The Interpreter and Translator Trainer 9 (2): 156–172. https://doi.org/10.1080/1750399x.2015.1051768 .

Orlando, M. 2019. Training and educating interpreter and translator trainers as practitioners-researchers-teachers. The Interpreter and Translator Trainer 13 (3): 216–232. https://doi.org/10.1080/1750399x.2019.1656407 .

Pan, J., and J. Yan. 2012. Learner variables and problems perceived by students: an investigation of a college interpreting programme in China. Perspectives: Studies in Translatology 20 (2): 199–218. https://doi.org/10.1080/0907676x.2011.590594 .

Parvaresh, S., H. Pirnajmuddin, and A. Hesabi. 2019. Student resistance in a literary translation classroom: A study within an instructional conversion experience from a transmissionist approach to a transformationist one. The Interpreter and Translator Trainer 13 (2): 132–151. https://doi.org/10.1080/1750399x.2018.1558724 .

Pietrzak, P. (2018). The effects of students’ self-regulation on translation quality. Babel 64 (5–6): 819–839(21). https://doi.org/10.1075/babel.00064.pie .

Pinto, M., and D. Sales. 2007. A case study research for user-centred information literacy instruction: information behaviour of translation trainees’. Journal of Information Science 33 (5): 531–49.

Pinto, M., and D. Sales. 2008. Towards user-centred information literacy instruction in translation. The Interpreter and Translator Trainer 2 (1): 47–74. https://doi.org/10.1080/1750399x.2008.10798766 .

Pisanski Peterlin, A. 2013. Attitudes towards English as an academic Lingua Franca in translation. The Interpreter and Translator Trainer 7 (2): 195–216. https://doi.org/10.1080/13556509.2013.10798851 .

Pokorn, N. 2009. Natives or non-natives? That is the question…teachers of translation into language B. The Interpreter and Translator Trainer 3 (2): 189–208. https://doi.org/10.1080/1750399x.2009.10798788 .

Pym, A. 2013. Research skills in translation studies: What we need training in. Across Languages and Cultures 14 (1): 1–14. https://doi.org/10.1556/Acr.14.2013.1.1 .

Quezada, C., and A. Westmacott. 2019. Reflections of L1 reading comprehension skills in university academic grades for an undergraduate translation programme. The Interpreter and Translator Trainer 13 (4): 426–441. https://doi.org/10.1080/1750399x.2019.1603135 .

Rea-Dickens, P., and K.P. Germaine (eds.). 1998. Managing evaluation and innovation in language teaching: Building bridges . London: Longman.

Richards, J. 2001. Curriculum development in language teaching . Cambridge: Cambridge University Press.

Richards, J., and R. Schmidt. 2010. Longman dictionary of language teaching and applied linguistics . Pearson Education Limited.

Rosiers, A., J. Eyckmans, and D. Bauwens. 2011. A story of attitudes and aptitudes? Investigating individual difference variables within the context of interpreting. Interpreting 13 (1): 53–69. https://doi.org/10.1075/intp.13.1.04ros .

Rosiers, A., and J. Eyckmans. 2017. Birds of a feather? A comparison of the personality profiles of aspiring interpreters and other language experts. Across Languages and Cultures 18 (1): 29–51. https://doi.org/10.1556/084.2017.18.1.2 .

Rothwell, A., and T. Svoboda. 2019. Tracking translator training in tools and technologies: Findings of the EMT survey 2017. The Journal of Specialised Translation 32: 26–60.

Salmi, L., and T. Kinnunen. 2015. Training translators for accreditation in Finland. The Interpreter and Translator Trainer 9 (2): 229–242. https://doi.org/10.1080/1750399x.2015.1051772 .

Schjoldager, A., K. Rasmussen, and C. Thomsen. 2008. Précis-writing, revision and editing: Piloting the European master in translation. Meta: Translators’ Journal 53 (4): 798–813. https://doi.org/10.7202/019648ar .

Schnell, B., and N. Rodríguez. 2017. Ivory tower vs. workplace reality: Employability and the T&I curriculum–balancing academic education and vocational requirements: A study from the employers’ perspective. The Interpreter and Translator Trainer 11 (2–3): 160–186. https://doi.org/10.1080/1750399x.2017.1344920 .

Shaw, S. 2011. Cognitive and motivational contributors to aptitude: A study of spoken and signed language interpreting students. Interpreting 13 (1): 70–84. https://doi.org/10.1075/intp.13.1.05sha .

Shaw, S., and G. Hughes. 2006. Essential characteristics of sign language interpreting students: Perspectives of students and faculty. Interpreting 8 (2): 195–221. https://doi.org/10.1075/intp.8.2.05sha .

Solová, R. 2015. The polish sworn translator: Current training profile and perspectives. The Interpreter and Translator Trainer 9 (2): 243–259. https://doi.org/10.1080/1750399x.2015.1051773 .

Stern, L., and X. Liu. 2019. See you in court: How do Australian institutions train legal interpreters? The Interpreter and Translator Trainer 13 (4): 361–389. https://doi.org/10.1080/1750399x.2019.1611012 .

Su, W. 2019. Interpreting quality as evaluated by peer students. The Interpreter and Translator Trainer 13 (2): 177–189. https://doi.org/10.1080/1750399x.2018.1564192 .

Tao, Y. 2019. The development of translation and interpreting curriculum in China’s mainland: A historical overview. In Translation Studies in China. New Frontiers in Translation Studies , ed. Z. Han and D. Li. Singapore: Springer.

Timarová, S., and H. Salaets. 2011. Learning styles, motivation and cognitive flexibility in interpreter training: Self-selection and aptitude. Interpreting 13 (1): 31–52. https://doi.org/10.1075/intp.13.1.03tim .

Tomassini, E. 2012. Healthcare interpreting In Italy: Current needs and proposals to promote collaboration between universities and healthcare services. The Interpreters’ Newsletter 17: 39–54.

West, R. 1994. Needs analysis in language teaching. Language Teaching 27 (1): 1–19.

Williamson, A. 2016. Lost in the shuffle: Deaf-parented interpreters and their paths to interpreting careers. International Journal of Interpreter Education 8 (1): 4–22.

Wu, Z. 2016. Towards understanding interpreter trainees’ (de)motivation: An exploratory study. Translation & Interpreting 8 (2): 13–25.

Wu, D., L. Jun Zhang, and L. Wei. 2019a. Developing translator competence: Understanding trainers’ beliefs and training practices. The Interpreter and TranslatorTrainer 13 (3): 233–254. https://doi.org/10.1080/1750399x.2019.1656406 .

Wu, D., L. Wei, and A. Mo. 2019b. Training translation teachers in an initial teacher education programme: A self-efficacy beliefs perspective. Perspectives 27 (1): 74–90. https://doi.org/10.1080/0907676x.2018.1485715 .

Yan, J., and H. Wang. 2012. Second language writing anxiety and translation: Performance in a Hong Kong tertiary translation class. The Interpreter and Translator Trainer 6 (2): 171–194. https://doi.org/10.1080/13556509.2012.10798835 .

You, Z. 2018. British university students’ attitude and usage of Google translate (L2 Japanese). Paper presented at the conference on Google Translate & Modern Languages Education, University of Nottingham, June 29 2018.

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21 Research Objectives Examples (Copy and Paste)

Research objectives refer to the definitive statements made by researchers at the beginning of a research project detailing exactly what a research project aims to achieve.

These objectives are explicit goals clearly and concisely projected by the researcher to present a clear intention or course of action for his or her qualitative or quantitative study. 

Research objectives are typically nested under one overarching research aim. The objectives are the steps you’ll need to take in order to achieve the aim (see the examples below, for example, which demonstrate an aim followed by 3 objectives, which is what I recommend to my research students).

Research Objectives vs Research Aims

Research aim and research objectives are fundamental constituents of any study, fitting together like two pieces of the same puzzle.

The ‘research aim’ describes the overarching goal or purpose of the study (Kumar, 2019). This is usually a broad, high-level purpose statement, summing up the central question that the research intends to answer.

Example of an Overarching Research Aim:

“The aim of this study is to explore the impact of climate change on crop productivity.” 

Comparatively, ‘research objectives’ are concrete goals that underpin the research aim, providing stepwise actions to achieve the aim.

Objectives break the primary aim into manageable, focused pieces, and are usually characterized as being more specific, measurable, achievable, relevant, and time-bound (SMART).

Examples of Specific Research Objectives:

1. “To examine the effects of rising temperatures on the yield of rice crops during the upcoming growth season.” 2. “To assess changes in rainfall patterns in major agricultural regions over the first decade of the twenty-first century (2000-2010).” 3. “To analyze the impact of changing weather patterns on crop diseases within the same timeframe.”

The distinction between these two terms, though subtle, is significant for successfully conducting a study. The research aim provides the study with direction, while the research objectives set the path to achieving this aim, thereby ensuring the study’s efficiency and effectiveness.

How to Write Research Objectives

I usually recommend to my students that they use the SMART framework to create their research objectives.

SMART is an acronym standing for Specific, Measurable, Achievable, Relevant, and Time-bound. It provides a clear method of defining solid research objectives and helps students know where to start in writing their objectives (Locke & Latham, 2013).

Each element of this acronym adds a distinct dimension to the framework, aiding in the creation of comprehensive, well-delineated objectives.

Here is each step:

• Specific : We need to avoid ambiguity in our objectives. They need to be clear and precise (Doran, 1981). For instance, rather than stating the objective as “to study the effects of social media,” a more focused detail would be “to examine the effects of social media use (Facebook, Instagram, and Twitter) on the academic performance of college students.”
• Measurable: The measurable attribute provides a clear criterion to determine if the objective has been met (Locke & Latham, 2013). A quantifiable element, such as a percentage or a number, adds a measurable quality. For example, “to increase response rate to the annual customer survey by 10%,” makes it easier to ascertain achievement.
• Achievable: The achievable aspect encourages researchers to craft realistic objectives, resembling a self-check mechanism to ensure the objectives align with the scope and resources at disposal (Doran, 1981). For example, “to interview 25 participants selected randomly from a population of 100” is an attainable objective as long as the researcher has access to these participants.
• Relevance : Relevance, the fourth element, compels the researcher to tailor the objectives in alignment with overarching goals of the study (Locke & Latham, 2013). This is extremely important – each objective must help you meet your overall one-sentence ‘aim’ in your study.
• Time-Bound: Lastly, the time-bound element fosters a sense of urgency and prioritization, preventing procrastination and enhancing productivity (Doran, 1981). “To analyze the effect of laptop use in lectures on student engagement over the course of two semesters this year” expresses a clear deadline, thus serving as a motivator for timely completion.

You’re not expected to fit every single element of the SMART framework in one objective, but across your objectives, try to touch on each of the five components.

Research Objectives Examples

1. Field: Psychology

Aim: To explore the impact of sleep deprivation on cognitive performance in college students.

• Objective 1: To compare cognitive test scores of students with less than six hours of sleep and those with 8 or more hours of sleep.
• Objective 2: To investigate the relationship between class grades and reported sleep duration.
• Objective 3: To survey student perceptions and experiences on how sleep deprivation affects their cognitive capabilities.

2. Field: Environmental Science

Aim: To understand the effects of urban green spaces on human well-being in a metropolitan city.

• Objective 1: To assess the physical and mental health benefits of regular exposure to urban green spaces.
• Objective 2: To evaluate the social impacts of urban green spaces on community interactions.
• Objective 3: To examine patterns of use for different types of urban green spaces. 

3. Field: Technology

Aim: To investigate the influence of using social media on productivity in the workplace.

• Objective 1: To measure the amount of time spent on social media during work hours.
• Objective 2: To evaluate the perceived impact of social media use on task completion and work efficiency.
• Objective 3: To explore whether company policies on social media usage correlate with different patterns of productivity.

4. Field: Education

Aim: To examine the effectiveness of online vs traditional face-to-face learning on student engagement and achievement.

• Objective 1: To compare student grades between the groups exposed to online and traditional face-to-face learning.
• Objective 2: To assess student engagement levels in both learning environments.
• Objective 3: To collate student perceptions and preferences regarding both learning methods.

5. Field: Health

Aim: To determine the impact of a Mediterranean diet on cardiac health among adults over 50.

• Objective 1: To assess changes in cardiovascular health metrics after following a Mediterranean diet for six months.
• Objective 2: To compare these health metrics with a similar group who follow their regular diet.
• Objective 3: To document participants’ experiences and adherence to the Mediterranean diet.

6. Field: Environmental Science

Aim: To analyze the impact of urban farming on community sustainability.

• Objective 1: To document the types and quantity of food produced through urban farming initiatives.
• Objective 2: To assess the effect of urban farming on local communities’ access to fresh produce.
• Objective 3: To examine the social dynamics and cooperative relationships in the creating and maintaining of urban farms.

7. Field: Sociology

Aim: To investigate the influence of home offices on work-life balance during remote work.

• Objective 1: To survey remote workers on their perceptions of work-life balance since setting up home offices.
• Objective 2: To conduct an observational study of daily work routines and family interactions in a home office setting.
• Objective 3: To assess the correlation, if any, between physical boundaries of workspaces and mental boundaries for work in the home setting.

8. Field: Economics

Aim: To evaluate the effects of minimum wage increases on small businesses.

• Objective 1: To analyze cost structures, pricing changes, and profitability of small businesses before and after minimum wage increases.
• Objective 2: To survey small business owners on the strategies they employ to navigate minimum wage increases.
• Objective 3: To examine employment trends in small businesses in response to wage increase legislation.

9. Field: Education

Aim: To explore the role of extracurricular activities in promoting soft skills among high school students.

• Objective 1: To assess the variety of soft skills developed through different types of extracurricular activities.
• Objective 2: To compare self-reported soft skills between students who participate in extracurricular activities and those who do not.
• Objective 3: To investigate the teachers’ perspectives on the contribution of extracurricular activities to students’ skill development.

10. Field: Technology

Aim: To assess the impact of virtual reality (VR) technology on the tourism industry.

• Objective 1: To document the types and popularity of VR experiences available in the tourism market.
• Objective 2: To survey tourists on their interest levels and satisfaction rates with VR tourism experiences.
• Objective 3: To determine whether VR tourism experiences correlate with increased interest in real-life travel to the simulated destinations.

11. Field: Biochemistry

Aim: To examine the role of antioxidants in preventing cellular damage.

• Objective 1: To identify the types and quantities of antioxidants in common fruits and vegetables.
• Objective 2: To determine the effects of various antioxidants on free radical neutralization in controlled lab tests.
• Objective 3: To investigate potential beneficial impacts of antioxidant-rich diets on long-term cellular health.

12. Field: Linguistics

Aim: To determine the influence of early exposure to multiple languages on cognitive development in children.

• Objective 1: To assess cognitive development milestones in monolingual and multilingual children.
• Objective 2: To document the number and intensity of language exposures for each group in the study.
• Objective 3: To investigate the specific cognitive advantages, if any, enjoyed by multilingual children.

13. Field: Art History

Aim: To explore the impact of the Renaissance period on modern-day art trends.

• Objective 1: To identify key characteristics and styles of Renaissance art.
• Objective 2: To analyze modern art pieces for the influence of the Renaissance style.
• Objective 3: To survey modern-day artists for their inspirations and the influence of historical art movements on their work.

14. Field: Cybersecurity

Aim: To assess the effectiveness of two-factor authentication (2FA) in preventing unauthorized system access.

• Objective 1: To measure the frequency of unauthorized access attempts before and after the introduction of 2FA.
• Objective 2: To survey users about their experiences and challenges with 2FA implementation.
• Objective 3: To evaluate the efficacy of different types of 2FA (SMS-based, authenticator apps, biometrics, etc.).

15. Field: Cultural Studies

Aim: To analyze the role of music in cultural identity formation among ethnic minorities.

• Objective 1: To document the types and frequency of traditional music practices within selected ethnic minority communities.
• Objective 2: To survey community members on the role of music in their personal and communal identity.
• Objective 3: To explore the resilience and transmission of traditional music practices in contemporary society.

16. Field: Astronomy

Aim: To explore the impact of solar activity on satellite communication.

• Objective 1: To categorize different types of solar activities and their frequencies of occurrence.
• Objective 2: To ascertain how variations in solar activity may influence satellite communication.
• Objective 3: To investigate preventative and damage-control measures currently in place during periods of high solar activity.

17. Field: Literature

Aim: To examine narrative techniques in contemporary graphic novels.

• Objective 1: To identify a range of narrative techniques employed in this genre.
• Objective 2: To analyze the ways in which these narrative techniques engage readers and affect story interpretation.
• Objective 3: To compare narrative techniques in graphic novels to those found in traditional printed novels.

18. Field: Renewable Energy

Aim: To investigate the feasibility of solar energy as a primary renewable resource within urban areas.

• Objective 1: To quantify the average sunlight hours across urban areas in different climatic zones. 
• Objective 2: To calculate the potential solar energy that could be harnessed within these areas.
• Objective 3: To identify barriers or challenges to widespread solar energy implementation in urban settings and potential solutions.

19. Field: Sports Science

Aim: To evaluate the role of pre-game rituals in athlete performance.

• Objective 1: To identify the variety and frequency of pre-game rituals among professional athletes in several sports.
• Objective 2: To measure the impact of pre-game rituals on individual athletes’ performance metrics.
• Objective 3: To examine the psychological mechanisms that might explain the effects (if any) of pre-game ritual on performance.

20. Field: Ecology

Aim: To investigate the effects of urban noise pollution on bird populations.

• Objective 1: To record and quantify urban noise levels in various bird habitats.
• Objective 2: To measure bird population densities in relation to noise levels.
• Objective 3: To determine any changes in bird behavior or vocalization linked to noise levels.

21. Field: Food Science

Aim: To examine the influence of cooking methods on the nutritional value of vegetables.

• Objective 1: To identify the nutrient content of various vegetables both raw and after different cooking processes.
• Objective 2: To compare the effect of various cooking methods on the nutrient retention of these vegetables.
• Objective 3: To propose cooking strategies that optimize nutrient retention.

The Importance of Research Objectives

The importance of research objectives cannot be overstated. In essence, these guideposts articulate what the researcher aims to discover, understand, or examine (Kothari, 2014).

When drafting research objectives, it’s essential to make them simple and comprehensible, specific to the point of being quantifiable where possible, achievable in a practical sense, relevant to the chosen research question, and time-constrained to ensure efficient progress (Kumar, 2019). 

Remember that a good research objective is integral to the success of your project, offering a clear path forward for setting out a research design , and serving as the bedrock of your study plan. Each objective must distinctly address a different dimension of your research question or problem (Kothari, 2014). Always bear in mind that the ultimate purpose of your research objectives is to succinctly encapsulate your aims in the clearest way possible, facilitating a coherent, comprehensive and rational approach to your planned study, and furnishing a scientific roadmap for your journey into the depths of knowledge and research (Kumar, 2019). 

Kothari, C.R (2014). Research Methodology: Methods and Techniques . New Delhi: New Age International.

Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners .New York: SAGE Publications.

Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives. Management review, 70 (11), 35-36.

Locke, E. A., & Latham, G. P. (2013). New Developments in Goal Setting and Task Performance . New York: Routledge.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Social-Emotional Learning (Definition, Examples, Pros & Cons)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is Educational Psychology?
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is IQ? (Intelligence Quotient)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University

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• Review Article
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• Published: 20 April 2020

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders

• C. Scarpazza 1 , 2 ,
• L. Baecker 1 ,
• R. Garcia-Dias 1 ,
• W. H. L. Pinaya 1 , 3 ,
• S. Vieira 1 &
• A. Mechelli 1  

Translational Psychiatry volume  10 , Article number:  107 ( 2020 ) Cite this article

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• Neuroscience
• Psychiatric disorders

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an “ideal” neuroimaging-based clinical tool for brain disorders.

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Introduction.

Brain-based disorders, including psychiatric and neurological illnesses, represent 10.4% of the global burden of disease 1 , and their prevalence within the general population is thought to be increasing 2 . While the past few decades have seen significant progress in our biological understanding of these disorders, this has had little or no impact on real-world clinical practice 3 , 4 , 5 . This is especially the case in clinical psychiatry, where diagnostic and prognostic assessment is still based on self-reports and clinical ratings, which are associated with low inter-rater agreement and accuracy 6 . It is recognized that patients suffering from psychiatric and neurological illnesses could benefit from the translation of the research findings into clinical practice. The key question for researchers and clinicians is how to enable this 7 , 8 .

Over the past two decades, scientists have invested many resources in the use of brain-imaging to develop objective tests for detecting brain disorders, monitoring their progression over time and optimizing treatment. This has led to several promising findings. For example, in the field of psychiatry, structural neuroimaging has revealed widespread neuroanatomical alterations, including both transdiagnostic and disorder-specific effects 9 , 10 , 11 . Additionally, neuroanatomical measures have been found to account for up to 40% of the variance in clinical outcome, and can even explain some of this variance where clinical variables (e.g. diagnosis) fail to do so 12 , 13 , 14 . These findings have led to the suggestion that structural neuroimaging could be used to develop objective measures of psychiatric disease, in contrast with current nosological criteria which are susceptible to subjective bias 7 .

However, so far we have not been able to translate the plethora of promising findings into clinically useful imaging-based tests 5 , 15 , 16 , 17 . One of the main reasons for the current gap between research and clinical practice, is that the former has been dominated by analytical methods that only allow statistical inferences at group-level (e.g. how does the brain differ between a group of people with psychosis and a group of healthy controls?); whilst a clinician has to make diagnostic and treatment decisions at the level of the individual. In recent years, a growing number of studies have attempted to address this issue by using alternative analytical methods that allow statistical inferences at the level of the single case. A large proportion of these studies have been employing machine-learning methods to make inferences at the levels of the individual based on structural 18 , 19 or functional 20 , 21 neuroimaging data. This has resulted in a number of encouraging findings 22 , 23 . For example, machine-learning methods appear to be effective in differentiating between patients with brain illness and healthy controls, and in predicting the onset of illness and response to treatment 12 , 22 , 23 . Although this is still an emerging area of research, there is compelling evidence that neuroimaging data allow for more accurate diagnostic and prognostic inferences compared to the use of clinical and psychometric data alone 12 .

Following these encouraging findings, some research teams have been developing imaging-based tools for making inferences at the level of the individual 24 , 25 , 26 , 27 . Through these tools, clinicians can upload the brain images of individual patients and receive an automatic report of the brain abnormalities detected. These tools differ greatly with respect to their specific purpose (e.g. what disease is being targeted), their technical characteristics (e.g. what is the underlying statistical model), their robustness (e.g. how the tool was validated) and their availability (e.g. freely vs. commercially available). At present there is no single resource which presents all available tools and systematically compares their aims and characteristics; this means that it can be difficult for a clinician or a researcher to identify the most appropriate tool. In addition, in the absence of a systematic review of their strengths and limitations, the real translational potential of the existing tools is still unclear. To address this gap, we conducted a systematic review of available neuroimaging-based clinical tools for making inferences at single-subject level. Our first aim was to describe and compare how these tools have been developed and validated, with the ultimate goal of assessing their translational potential in real-world clinical settings. Our second aim was to use the findings to develop a checklist of the pivotal characteristics that should be included in an ideal imaging-based clinical tool for brain disorders. We hope that this review will help clinicians and researchers appreciate the aims, strengths, and limitations of the available tools and select the most appropriate option for their investigations.

Materials and methods

Studies selection.

As the results of the current review might have health-related implications, the protocol of this review has been registered to the International Prospective Register of Systematic Reviews (PROSPERO—Registration Number: CRD42019127819). In accordance with the PRISMA guidelines 28 , 29 , an in-depth search was conducted on PubMed and Google Scholar databases up to February 2019. The following terms were used: (brain AND (MRI OR neuroimaging OR “magnetic resonance”) AND (“clinical tool”) AND (psychiatric OR psychiatry OR neurological OR neurology OR disease OR disorder)). All papers describing a neuroimaging-based tool developed to detect brain abnormalities in brain disorders at the level of the individual, regardless of the diagnosis, were included. Furthermore, additional relevant studies were found using different strategies. These included using the “related articles” function of the PubMed database; tracing the references from the identified papers; tracing the key references on the tool websites; and emailing the providers of the clinical tools.

Inclusion and exclusion criteria

The following inclusion criteria were used: (i) articles presenting a neuroimaging-based clinical tool; (ii) articles presenting a tool aimed at detecting abnormalities in the brain (i.e. studies presenting a tool for detecting abnormalities in other organs, for instance the heart, were excluded); (iii) articles presenting a validation of the algorithm or technology that underlie the tool (i.e. studies applying an already validated clinical tool were excluded); (iv) articles published as original articles in peer-reviewed academic journals or conference proceedings (posters from conferences were excluded); (v) articles published or available in English.

Articles were excluded from the review according to the following a priori exclusion criteria: (i) articles that present software for analyzing neuroimaging data without a clear implementation in a translational tool (e.g. Statistical Parametric Mapping 18 , 19 ); (ii) articles reporting studies that use non-human subjects; and (iii) studies that present clinical tools that are yet to be released.

According to our first exclusion criterion, we excluded platforms which allow the storage and analysis of individual MRI scans, using software such as Freesurfer 30 , Sienax 31 , or FSL 32 , but do not provide a clinically meaningful report including an estimate of neuroanatomical abnormalities at the level of the individual. One example is QMENTA ( https://www.qmenta.com/ ), a cloud-based platform where different neuroimaging modalities (i.e. structural MRI, functional MRI, diffusion tensor imaging, positron emission tomography) can be stored and a different of different statistical analyses can be carried out. For instance, using QMENTA, researchers can investigate gray matter (GM) volume, cortical thickness, structural and functional connectivity, and ventricular volumetry, just to name a few of the multiple analyses which can be implemented via this platform. The advantage of using a platform such as QMENTA is the possibility to run multiple analyses simultaneously on a cloud thereby saving time. However, QMENTA does not provide researchers and clinicians with individualized reports indicating whether or not the brain under investigation deviates from those of healthy controls and what specific alternations might be driving this conclusion.

According to the same exclusion criterion, we also excluded ASSESSA PML ( https://ixico.com/technology/data-platforms/assessa-platform/ ), a platform allowing neurologists to transfer clinical and neuroimaging data to expert neuroradiologists, who will visually inspect the scans to detect the presence of progressive multifocal leukoencephalopathy (PML), an opportunistic infection of the brain emerging as an adverse event of pharmacotherapy to treat multiple sclerosis (MS) 33 . ASSESSA PML was excluded from the current review as it is not a clinical tool that automatically extracts clinically relevant information from neuroimaging data.

Data extraction

Two authors (C.S. and M.J.H.) extracted and checked the data independently. An additional member of the team double-checked the data in case of discordance between the first two extractions. An independent researcher oversaw the entire search procedure and randomly selected some of the articles for a random double-check. In this process, no critical issues were detected by the independent researcher. A database was created including the following characteristics: general information (authors, year of publication, name of the tool, website) and technical details regarding the tool (type of images analyzed, type of analysis performed, number of subjects used to create and validate the tool, image source, i.e. the dataset used to create and validate the algorithm, group of patients that would benefit from the tool, brain regions analyzed by the tool, validation strategy, abnormality inference strategy). Additional information regarding each tool was also recorded, including how to access it, how to use it, how the results are reported, time from images upload to report, whether the tool has been licensed, strengths and limitations.

The literature screening and final selection were performed according to the PRISMA guidelines 28 , 29 . This procedure is summarized in the flow diagram (Fig. 1 ). Applying the PRISMA procedure, a total of eight tools from 24 original articles have been included in the systematic review.

This figure represents the inclusion procedure used to select relevant articles following the PRISMA guidelines 28 , 29 .

Excluded tools

According to the PRISMA guidelines, inclusion and exclusion criteria must be decided before running the systematic search. In the current review, an additional exclusion criterion was added a posteriori: we decided to exclude tools that are no longer available. This decision was motivated by the following reasons. First, when a tool was no longer available, there was no tool-related website either; this made it impossible to collect some of the information required for the present review. Second, a tool that was no longer available was not relevant to our aim to help clinicians and researchers select the most appropriate option for their investigations. Based on this additional exclusion criterion, two tools were excluded.

The first one, ASSESSA, was initially developed to automatically provide a quantification of GM atrophy and white matter (WM) lesion volume. The focus of this tool was the quantification of hippocampal volume through the learning embeddings for atlas propagation (LEAP) 34 , an algorithm for the quantification of the regional volume which was developed to enrich clinical trials of Alzheimer’s disease in the pre-dementia phase. The clinical tool ASSESSA is no longer available.

The second tool to be excluded, called appMRI, was developed to allow for the automatic statistical analysis of hippocampal volume ( http://appmri.org/en/ ). The tool performed an automated segmentation using FreeSurfer software and then provided a numerical output of left and right hippocampal volumes, together with normative values generated using a reference database of age-matched healthy controls. As for ASSESSA, this tool is no longer available.

Included tools

Eight neuroimaging-based clinical tools were identified. Their technical characteristics are summarized in Table 1 , while more general information, including how to use each tool and their strengths and limitations, is reported in Table 2 .

Two of the eight tools (ADABOOST 35 and Qure 25 ) are designed to specifically perform a single type of analysis (hippocampus segmentation and gross abnormality identification, respectively). On the contrary, the other six tools (DIADEM 36 , 37 , Icobrain 38 , 39 , 40 , 41 , Jung Diagnostics 27 , 42 , 43 , NeuroQuant 24 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , Quantib 52 , 53 , volBrain 54 , 55 ) are designed to extract multiple types of information from the data and/or evaluate multiple disorders.

As reported in Table 2 , six of the eight tools obtained at least one certification for medical use (DIADEM, Icobrain, Jung Diagnostics, NeuroQuant, Quantib, Qure). The remaining two tools are not approved for medical use. In particular, ADABOOST 35 is present on the neuGrid platform 56 , a web portal which aims to provide automated algorithms to support the diagnostic assessment of individual patients with neurodegenerative disease from neuroimaging data. The second tool which is not approved for medical use is volBrain 26 , 54 , 55 . The website for this tool explicitly states that it was developed for research purposes, and as such does not hold any certification for medical use.

One tool (DIADEM 36 , 37 ) has no associated references describing the underlying methodology in detail. The references that are mentioned on the website 36 , 37 describe algorithms to perform parcellation and segmentation with better accuracy than previous approaches. However, it is not clear how are these algorithms are incorporated within the overall tool which performs several additional functions e.g. quantification and labeling. For this reason, we do not report the main characteristics of this tool in the following results description, as they are not present in any scientific reference.

Target disorders

All the identified clinical tools have been developed to support the diagnosis of neurological disorders. In particular, five tools are designed to provide quantitative support to the diagnosis of dementia and in particular of Alzheimer’s disease (ADABOOST 35 , Jung Diagnostistics 27 , 43 , NeuroQuant 45 , Quantib 53 , volBrain 26 , 54 ), mild cognitive impairment (MCI) (ADABOOST 35 , Jung Diagnostics 27 , NeuroQuant 48 ), or other forms of dementia (Jung Diagnostics 43 ). Furthermore, four tools are designed to support the diagnosis of MS (Icobrain 38 , 39 , 41 , Jung Diagnostics 42 , Quantib 52 , volBrain 55 ). In addition, one tool (NeuroQuant) has a parallel version called LesionQuant which has been developed to assist the diagnosis of MS. However, no reference to a scientific publication presenting this alternative version is available on the website. Two tools supported the diagnosis of traumatic brain injury (TBI) (Icobrain 40 and NeuroQuant 46 , 49 , 50 , 51 ). Finally, one tool can be used to provide support to the diagnosis of temporal lobe epilepsy (TLE) (NeuroQuant 44 , 47 ), and one tool (Qure 25 ) is designed to identify different types of intracranial hemorrhages and mass effects in the brain.

Type of analysis

All the identified clinical tools have been designed to perform a region of interest (ROI) analysis measuring pre-defined biomarkers for the target disorder. For instance, we know that dementia (in particular Alzheimer’s disease) is associated with atrophy of the hippocampus. Accordingly, two tools are specifically designed to focus on hippocampal volume as a biomarker of this disease (ADABOOST 35 and Jung Diagnostics 27 , 43 ). One additional tool is designed for the investigation of the hippocampus but has not been specifically validated in patients with dementia (volBrain 54 ). Other tools support the diagnosis of dementia through the quantification of both hippocampus volume and general atrophy (NeuroQuant 45 , 48 , Quantib 53 , volBrain 26 ). Finally, one tool performs atrophy quantification (Icobrain 41 ) but has only been validated in patients with MS. As dementia might also be associated with metabolic abnormalities, one tool (PETQuant, a variation of NeuroQuant) performs automatic analysis of metabolic and amyloid based positron emission tomography (PET) images. However, no references are available for this tool.

Similarly, the main pathognomonic feature for MS is the presence of inflammatory WM lesions 57 . Accordingly, five tools are designed to perform the segmentation of WM lesions and to calculate their volume (Icobrain 38 , 39 , JungDiagnostic 42 , NeuroQuant—no reference available, Quantib 52 , volBrain 55 ). In addition, as MS has recently been described to be associated with GM atrophy, one tool (Icobrain 41 ) also provides atrophy measurements in patients with MS.

Patients with TBI present with evident traumatic lesions in the brain. A tool (Icobrain 40 ) is therefore designed for intracranial lesion segmentation, cistern segmentation and the evaluation of midline shift. However, mild TBI is not associated with gross brain lesions but with subtle progressive atrophy 58 . Accordingly, a different tool (NeuroQuant 46 , 49 , 50 , 51 ) has been validated to detect atrophy, structures asymmetry and/or progressive atrophy in patients with TBI.

Patients with TLE are prone to suffer from Mesial Temporal Sclerosis (MTS), involving the loss of neurons and scarring of the deepest portion of the temporal lobe, in particular, the hippocampus 59 . One tool (NeuroQuant 44 , 47 ) is therefore designed to detect MTS in patients with TLE through the measurement of the hippocampus volume. Finally, one tool (Qure 25 ) identifies gross abnormalities such as tumors and strokes.

Brain imaging type

The vast majority of the identified tools analyze magnetic resonance images (MRI) data, in particular, T1-weighted images (ADABOOST 35 , Icobrain 38 , 39 , 41 , Jung Diagnostics 27 , 42 , 43 , NeuroQuant 44 , 45 , 47 , 48 , Quantib 52 , 53 , VolBrain 26 , 54 , 55 ). However, there are a few exceptions. Four tools also require the fluid attenuated inversion recovery (FLAIR) acquisition sequence for the segmentation of WM lesions (Icobrain 38 , 39 , 41 , LesionQuant, a parallel version of NeuroQuant with no reference available, Quantib 52 , volBrain 55 ). One tool (Qure 25 ) analyzes non-contrast computerized tomography (CT) scans, while one tool (Icobrain 40 ) requires CT scan in the case of suspected TBI. Finally, one tool (PETQuant) analyzes images acquired using positron emission tomography.

Validation datasets and strategies

All the identified tools can be used to perform a cross-sectional analysis, and thus can be applied to support the diagnosis. Two tools (Icobrain 38 , 41 and Neuroquant 46 ) have also been validated on longitudinal data to predict the natural course of the disease. No tools have been validated to predict the longitudinal response to treatment.

Most tools have been validated using MRI data collected from a single dataset, either freely or private. In a small number of cases, validation is based on the use of multiple datasets. For instance, Smeets et al. 41 (Icobrain for MS) used three datasets, two of which are private and the third one is publicly available 60 ; Ochs et al. 49 , Ross et al. 50 , 51 used data from healthy participants and patients with AD that were part of the ADNI dataset ( http://adni.loni.usc.edu/ ) in combination with scans from patients with TBI which were part of a private dataset; volBrain 26 , 54 , 55 was validated using healthy participants data from IXI ( http://brain-development.org/ ) and from additional publicly available datasets ( http://www.nitrc.org/projects/mni-hisub25 ; http://cobralab.ca/atlases ), AD patients data from OASIS ( http://www.oasis-brains.org/ ), infants data from BSTP ( http://brain-development.org ), MS data from the MSSEG 2016 ( https://www.hal.inserm.fr/inserm-01397806 ). Qure 25 was validated combining scans from 20 different private datasets in India. Finally, Biometrica MS 42 (the MS version of Jung Diagnostics) combined real and simulated data. In no case, the strategy adopted to deal with the problem of different scanners and/or different acquisition parameters has been described. The strategy used to validate the tools always consisted of comparing the tool performance with the performance of the gold standard. The gold standard is mainly of three types: a ROI manual delineation by an expert; the performance of previously available software; the performance of an expert radiologist in abnormality identification by visual inspection. The tools that have been validated using the first strategy (i.e. comparison with a manual delineation of ROI) are: ADABOOST 35 , Icobrain for TBI 40 , NeuroQuant for sub-cortical segmentation 45 , 48 , and Quantib for both sub-cortical structure 53 and WM lesions 52 . The tools that have been validated using the second strategy (i.e. comparison with previous software) are: Icobrain for WM lesion segmentation 38 , 39 , 41 , NeuroQuant for atrophy estimation 49 , volBrain for volumetry 26 , WM lesion segmentation 55 , and hippocampus estimation 54 . The tools that have been validated using the third strategy (i.e. comparison with visual inspection by an expert radiologist) are: Icobrain for WM lesion segmentation 38 , Jung Diagnostics for both hippocampus 27 , 43 and WM lesion identification 42 ; NeuroQuant for atrophy identification 44 , 47 , 50 , 51 . The only apparent exception is Qure 25 where the performance of the algorithm has been compared with the results of a medical report, which in turn relies on expert visual inspection as well as other clinical data.

Abnormality inference

All identified tools included a control group of disease-free individuals to compare the pathological brain. Five out of the eight tools (ADABOOST 35 ; Icobrain 38 , 39 , 40 ; Quantib 52 , 53 , Qure 25 , Jung Diagnostics 27 , 43 ) rely on machine-learning algorithms to detect brain abnormalities as statistical deviation from the average healthy brain. Two tools rely on classical statistics to identify brains whose structures are statistically different in volume from the analogous structure in the average healthy brain: volBrain 26 , 54 , 55 and NeuroQuant 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 detect abnormalities if a brain region volume falls below the 5th percentile or above the 95th percentile of the same region in the average brain.

Strengths and limitations

The identified tools are characterized by important strengths (see Table 2 for a tool specific description of the strengths and limitations). First, the majority of the tools rely on advanced machine-learning algorithms that offer superior ability to detect complex and distributed patterns in the data 61 , 62 (ADABOOST 35 ; Icobrain 38 , 39 , 40 ; Quantib 52 , 53 ; Qure 25 ; Jung Diagnostics 27 , 43 ). Second, most of the tools have been licensed for medical use, and this undoubtedly presents an important step toward their translational application in real-world clinical settings. Third, the time from image upload to the report receipt is less than an hour. For instance, using volBrain, results are available in 12 min; using NeuroQuant in 8 min; using Icometrix in 1 h.

However, these tools are also characterized by important limitations. First, they are validated for neurological disorders only; no tool is available for supporting the diagnosis of psychiatric disorders to date. Second, each tool performs a ROI analysis to investigate a single disorder of interest; no tool is available for investigating multiple disorders. Third, all these tools but one (Qure 25 , which relies on 291,732 images) have been validated on a small number of brain images. Although some of them used fairly large datasets to develop some normative model that could be used to detect abnormalities (e.g. n  = 200 35 for ADABOOST; n  = 600 for volBrain 26 ), the dataset used for validating such model tended to be much smaller ( n  = 7 MCI, n  = 7 AD for ADABOOST 35 ; n  = 10 AD for volBrain 26 ). Finally, an important limitation common to all the available tools is that none of them account for inter-scanner variability resulting from differences in scanner provider, magnetic field and acquisition parameters. This is of crucial importance to develop flexible tools that are generalizable to “unseen” scanners i.e. scanners that were not used to train the tool.

The current review focused on the description of neuroimaging-based analytical tools that are available to support the clinical assessment of brain-based disorders. The primary aim was to describe and compare how these tools have been developed and validated. The second aim was to use the findings to develop a checklist of the pivotal characteristics that should be included in an ideal imaging-based clinical tool. Through a systematic search of the literature, eight clinical tools were identified. The most important aspects of these tools are discussed below.

First, the available tools are targeted towards neurological disorders only. In particular, most of them were developed to assist in the diagnosis of Alzheimer’s disease and/or MS. In contrast, we could not find any tools to support the clinical assessment of psychiatric disorders. This could be explained by the current paucity of reliable imaging-based biomarkers in psychiatric disorders, where neuroanatomical alternations tend to be subtle and widespread relative to neurological disorders 63 . Second, the available tools rely on the measurement/quantification of putative biomarkers that are pathognomonic for the neurological disorder they are validated for (i.e. hippocampus volume or GM atrophy for dementia; WM hyperintensities for MS). On the one hand, this aspect is of extreme importance, as it means the tools extract the relevant information in an automated manner and provide outputs that are not affected by subjective bias. On the other hand, one could argue that the actual clinical utility of these tools is limited, because all of them have been developed to detect neurological disorders where the diagnostic accuracy is already very good. Third, all tools have been validated by comparing their performance with a gold standard, which can be of three types: (a) the performance of human experts in the manual delineation of ROI; (b) the performance of previously available software; (c) the identification of brain pathology by visual inspection. Fourth, most of the tools were trained in a single dataset, which can result in poor generalizability to unseen scanners. Related to this point, all of the tools were developed without making an explicit attempt to tackle the bias resulting from inter-scanner variability. Fifth, the tools, with few exceptions, have been created and validated using a small number of individuals, a limitation with potential implications for their reliability and generalizability. Sixth, the tools mainly rely on two strategies to detect brain abnormalities: (a) application of multivariate machine-learning algorithms to compare the patient’s brain structure with the average healthy brain (most frequent); (b) univariate comparison of the patient’s data with the average healthy brain, for instance using percentiles (5° or 95°) or confidence intervals as cut-off for detecting abnormalities.

Adapting existing tools to psychiatric disorders: challenges

Could the existing tools be adapted to psychiatric disorders? There are many reasons why such adaptation might be challenging.

First, there are no established imaging-based biomarkers for psychiatric diagnosis 4 , 5 . For example, there is no single brain alteration that identifies psychosis with high sensitivity and specificity. Furthermore, the results obtained when comparing groups of psychiatric individuals against a group of healthy controls are usually unspecific. For instance, decreased GM volume of the frontal lobe has been found in schizophrenia 64 , depression 65 , PTSD 66 ; this might explain the presence of cross-cutting symptoms across psychiatric disorders. Therefore, the existing tools, which analyze specific biomarkers for neurological disorders, might be difficult to adapt to psychiatric disorders.

Second, the absence of biomarkers makes the diagnosis of psychiatric disorders quite unreliable, and consequently, it can be problematic to use diagnostic labels as the gold standard to validate a tool. Thus, strategies used to validate the existing tools would be difficult to implement in the case of psychiatric disorders since: (i) there is no relevant ROIs that can be manually traced; (ii) there are no software that reliably identifies psychiatric individuals at the level of the single subjects; (iii) psychiatric pathology cannot be identified by brain visual inspection. To create a tool that can be reliably applied to psychiatric research, an alternative validation strategy and gold standard would need to be identified.

Third, we need to consider the issue of disease heterogeneity. Although both psychiatric and neurological disorders tend to be heterogeneous in terms of clinical presentation, naturalistic course of the illness and treatment response 67 , 68 , 69 , neurological disorders are characterized by more specific and reliable neural correlates than psychiatric disorders. For example, atrophy of the hippocampus in Alzheimer’s disorder is evident above and beyond the neuroanatomical heterogeneity of the disease. The same cannot be said for the neuroanatomical alterations that are typically observed in psychiatric disorders. Here, neuroanatomical alterations tend to be subtle and widespread, making the discrimination between normal heterogeneity and pathological heterogeneity more challenging 63 , 70 , 71 . This means that the adaptation of existing tools to psychiatric disorders would require careful consideration of the issue of heterogeneity 72 .

Finally, we need to pay attention to how statistical inferences about the presence/absence of neuroanatomical abnormalities are made. As the neural correlates of psychiatric disorders are subtle, diffuse and complex, abnormality inferences that rely on classical statistics (e.g. percentiles) are likely to be highly prone to false negative findings. When adapting the existing tools to psychiatric disorders, therefore, it would be appropriate to adopt statistical models that can detect high orders of complexity and abstraction in the data. In this scenario, the application of advanced machine-learning methods, such as convolutional neural networks, is a promising strategy 73 , 74 .

In short, if the scientific and clinical psychiatric community is still devoid of a neuroimaging-based clinical tool to enrich the diagnostic pathway, the main reason appears to be the complexity of the problem at hand. Compared to neurological disease, psychiatric disorders are characterized by higher levels of etiological, phenotypic and neurobiological overlap, and heterogeneity 75 ; this makes the task of developing reliable imaging-based biomarkers a significantly greater challenge.

What would an ideal clinical tool for brain disorders look like?

In this last section, we propose several pivotal characteristics that should be included in an ideal imaging-based clinical tool (graphically represented in Fig. 2 ) to assist the clinical assessment of psychiatric disorders.

From a region-of-interest to whole-brain approach : Existing tools for neurological disorders use a region-of-interest approach to detect localized alternations. Considering the subtle and widespread neural correlates of the psychiatric disorders 22 , 71 , 76 , the ideal clinical tool should not restrict its analysis to a single or few regions; instead, it should analyze the whole-brain to exploit all the available neuroanatomical information.

Accounting for disease heterogeneity : As etiological, neurobiological and phenotypic heterogeneity is a key aspect of brain disorders 67 , 70 , 77 , the ideal tool should be created and validated on a sample which is large enough to capture such variability. While the required number of subjects depends on the heterogeneity of the disease under investigation, this is likely to be in the order of hundreds or even thousands for most brain disorders. In addition the sample size should be large enough to allow the investigation of gender-specific and age-specific effects within a clinical population of interest. As the number of subjects used to create and validate the tool increases, so does the sample heterogeneity due to the loosening of inclusion criteria. On the one hand, higher levels of heterogeneity make the creation of an accurate tool more challenging, as the model needs to be able to distinguish between normal heterogeneity and pathological heterogeneity 78 , 79 . On the other hand, larger samples are more likely to have a normal distribution and be representative of the clinical population of interest, and as such carry greater translational potential in real-world clinical practice.

Accounting for inter-scanner variability : As the ideal clinical tool is supposed to handle MRI scans of individuals from different clinicians/hospitals/countries, it should be able to estimate and account for differences in scanner provider, magnetic strength field and acquisition parameters. This is especially important for psychiatric disorders, where the effects of interest are subtle and, therefore, inter-scanner variability can be much greater than disease-related variability 71 , 80 , 81 .

The importance of validation : Since the validation strategies used for neurological disorders—where we have a few established diagnostic biomarkers—cannot be applied to psychiatric disorders, it is of pivotal importance to identify an alternative strategy to validate the tool. A possible solution might be to switch the focus from diagnostic to prognostic assessment and establish a prospective link between neuroanatomical alterations and clinical outcomes 12 . As an example, studies have shown that neuroanatomical alternations in patients at high clinical risk of developing psychosis are predictive of future transition to the illness 82 ; as a further example, cortical folding defects in people with a first episode of psychosis have been found to be predictive of future response to pharmacological treatment 83 . The use of clinical outcome measures could, therefore, provide an alternative validation strategy for tools targeting psychiatric disorders.

Using advanced multivariate statistics to capture abstract and complex patterns in the data : As the neural correlates of psychiatric disorders are subtle and distributed, the ideal clinical tool should use multivariate rather than univariate algorithms. In addition, in light of current conceptualizations of psychiatric and neurological illnesses as network-level disorders of the brain 84 , 85 , the ideal clinical tool should be able to capture multivariate interactions with high levels of abstraction and complexity. There are several statistical and machine-learning methods which could be used to achieve this. For example, deep learning is a family of algorithms that can detect high orders of complexity and abstraction in the data and make inferences at the level of the individual with greater precision than ever before 62 . In light of these qualities, deep learning algorithms are attracting significant interest in neuroscience including psychiatric and neurological research 86 .

Informing diagnostic and prognostic assessment : The ideal tool would assist clinicians through the complex tasks of clinical assessment and prognostic decision-making. Thus, the tool should indicate the likelihood of a certain diagnosis or a certain clinical outcome. This could be achieved by matching the neuroanatomical abnormalities identified in a patient with the neuroanatomical alterations that are known to be associated with a certain psychiatric disorder (in the case of diagnostic inference) or a certain clinical outcome (in the case of prognostic inference). A high/low match score would indicate that an individual presents with neuroanatomical changes that are typical/atypical of a certain psychiatric or neurological disorder a certain clinical outcome.

This figure summarizes the characteristics of an ideal clinical tool to assist the clinical assessment of psychiatric disorders.

Conclusions

A pivotal aim of neuroimaging research is the development of clinical tools that can support clinical decision-making by producing accurate, objective, and real-time outputs from neuroimaging data 17 . The results of this review indicate that there is a very limited number of clinical tools available to support the diagnosis of neurological disorders, while there are none for psychiatric disorders. In addition, only two of the available tools have been validated using longitudinal datasets, and are therefore suitable for prognostic assessment. The majority of the available tools (4 out of 7) make use of multivariate machine-learning methods, which allow inferences at the level of the individual and as such open up new possibilities in personalized medicine 87 . However, the results of such methods should be interpreted with caution 22 as they can be over-optimistic due to a combination of small sample sizes and less-than-rigorous methodologies 78 . A further complication is that several genetic and environmental factors that can affect the structure of the human brain without necessarily leading to pathology 88 . This means one must avoid the pitfall of considering structural brain abnormalities pathological per se should be avoided; instead, researchers and clinicians must interpret the output of a machine-learning model in light of the patient’s clinical history and symptomatology 89 . A related consideration is that the ideal tool should not be limited to the examination of brain abnormalities, but might also benefit from the integration of potentially valuable information such as duration of illness and symptomatic presentation 90 .

In conclusion, we envisage a future in which imaging-based tests will complement traditional clinical assessments of psychiatric and neurological disorders, leading to biologically informed diagnosis, monitoring and treatment of individual patients. Before this vision can be realized, however, several outstanding challenges need to be addressed; these include, for example, the issues of neuroanatomical heterogeneity, inter-scanner variability, and validation. We hope the observations and suggestions included in the present article will help researchers realize this vision in the future.

Whiteford, H. A., Ferrari, A. J., Degenhardt, L., Feigin, V. & Vos, T. The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS ONE 10 , e0116820 (2015).

PubMed   PubMed Central   Google Scholar  

Global Burden of Disease Study C. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386 , 743–800 (2015).

Google Scholar  

Kapur, S., Phillips, A. G. & Insel, T. R. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol. Psychiatry 17 , 1174–1179 (2012).

CAS   PubMed   Google Scholar  

Perlis, R. H. Translating biomarkers to clinical practice. Mol. Psychiatry 16 , 1076–1087 (2011).

CAS   PubMed   PubMed Central   Google Scholar  

Prata, D., Mechelli, A. & Kapur, S. Clinically meaningful biomarkers for psychosis: a systematic and quantitative review. Neurosci. Biobehav. Rev. 45 , 134–141 (2014).

Regier, D. A. et al. DSM-5 field trials in the United States and Canada, Part II: test–retest reliability of selected categorical diagnoses. Am. J. Psychiatry 170 , 59–70 (2013).

PubMed   Google Scholar  

McGuire, P. et al. Can neuroimaging be used to predict the onset of psychosis? Lancet Psychiatry 2 , 1117–1122 (2015).

Chmielewski, M., Clark, L. A., Bagby, R. M. & Watson, D. Method matters: understanding diagnostic reliability in DSM-IV and DSM-5. J. Abnorm. Psychol. 124 , 764–769 (2015).

Gong, Q. et al. A transdiagnostic neuroanatomical signature of psychiatric illness. Neuropsychopharmacology 44 , 869–875 (2019).

Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72 , 305–315 (2015).

Mitelman, S. A. Transdiagnostic neuroimaging in psychiatry: a review. Psychiatry Res. 277 , 23–38 (2019).

Jollans, L. & Whelan, R. The clinical added value of imaging: a perspective from outcome prediction. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 1 , 423–432 (2016).

Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L. & Martin, A. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism. Proc. Natl Acad. Sci. USA 112 , E6699–E6706 (2015).

Siegle, G. J. et al. Toward clinically useful neuroimaging in depression treatment: prognostic utility of subgenual cingulate activity for determining depression outcome in cognitive therapy across studies, scanners, and patient characteristics. Arch. Gen. Psychiatry 69 , 913–924 (2012).

Dazzan, P. Neuroimaging biomarkers to predict treatment response in schizophrenia: the end of 30 years of solitude? Dialogues Clin. Neurosci. 16 , 491–503 (2014).

Savitz, J. B., Rauch, S. L. & Drevets, W. C. Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play. Mol. Psychiatry 18 , 528–539 (2013).

Woo, C. W., Chang, L. J., Lindquist, M. A. & Wager, T. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20 , 365–377 (2017).

Scarpazza, C. et al. When the single matters more than the group (II): addressing the problem of high false positive rates in single case voxel based morphometry using non-parametric statistics. Front. Neurosci. 10 , 6 (2016).

Scarpazza, C., Sartori, G., De Simone, M. S. & Mechelli, A. When the single matters more than the group: very high false positive rates in single case Voxel Based Morphometry. Neuroimage 70 , 175–188 (2013).

Laumann, T. O. et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 87 , 657–670 (2015).

Poldrack, R. A. et al. Long-term neural and physiological phenotyping of a single human. Nat. Commun. 6 , 8885 (2015).

Vieira, S. et al. Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence. Schizophr. Bull . https://doi.org/10.1093/schbul/sby189 (2019).

Orru, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G. & Mechelli, A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav Rev. 36 , 1140–1152 (2012).

Brewer, J. B. Fully-automated volumetric MRI with normative ranges: translation to clinical practice. Behav. Neurol. 21 , 21–28 (2009).

Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392 , 2388–2396 (2018).

Manjon, J. V. & Coupe, P. volBrain: an online MRI brain volumetry system. Front. Neuroinform. 10 , 30 (2016).

Suppa, P. et al. Fully automated atlas-based hippocampus volumetry for clinical routine: validation in subjects with mild cognitive impairment from the ADNI Cohort. J. Alzheimers Dis. 46 , 199–209 (2015).

Liberati, A. et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339 , b2700 (2009).

Moher, D. et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339 , b2535 (2009).

Fischl, B. FreeSurfer. Neuroimage 62 , 774–781 (2012).

Smith, S. M. et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17 , 479–489 (2002).

Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl. 1), S208–S219 (2004).

Major, E. O., Yousry, T. A. & Clifford, D. B. Pathogenesis of progressive multifocal leukoencephalopathy and risks associated with treatments for multiple sclerosis: a decade of lessons learned. Lancet Neurol. 17 , 467–480 (2018).

Wolz, R. et al. LEAP: learning embeddings for atlas propagation. Neuroimage 49 , 1316–1325 (2010).

Morra, J. H. et al. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage 43 , 59–68 (2008).

Cardoso, M. J. et al. Geodesic information flows. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012. Lecture Notes in Computer Science (eds Ayache, N. et al.) (Springer, Berlin, 2012).

Cardoso, M. J. et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34 , 1976–1988 (2015).

Jain, S. et al. Two time point MS lesion segmentation in brain MRI: an expectation-maximization framework. Front. Neurosci. 10 , 576 (2016).

Jain, S. et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin. 8 , 367–375 (2015).

Jain, S. et al. Automatic quantification of computed tomography features in acute traumatic brain injury. J. Neurotrauma https://doi.org/10.1089/neu.2018.6183 (2019).

Smeets, D. et al. Reliable measurements of brain atrophy in individual patients with multiple sclerosis. Brain Behav. 6 , e00518 (2016).

Spies, L. et al. Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis. Phys. Med. Biol. 58 , 8323–8337 (2013).

Suppa, P. et al. Fully automated atlas-based hippocampal volumetry for detection of Alzheimer’s disease in a memory clinic setting. J. Alzheimers Dis. 44 , 183–193 (2015).

Azab, M., Carone, M., Ying, S. H. & Yousem, D. M. Mesial temporal sclerosis: accuracy of NeuroQuant versus neuroradiologist. Am. J. Neuroradiol. 36 , 1400–1406 (2015).

Brewer, J. B., Magda, S., Airriess, C. & Smith, M. E. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. Am. J. Neuroradiol. 30 , 578–580 (2009).

Brezova, V. et al. Prospective longitudinal MRI study of brain volumes and diffusion changes during the first year after moderate to severe traumatic brain injury. Neuroimage Clin. 5 , 128–140 (2014).

Farid, N. et al. Temporal lobe epilepsy: quantitative MR volumetry in detection of hippocampal atrophy. Radiology 264 , 542–550 (2012).

Kovacevic, S., Rafii, M. S. & Brewer, J. B., The Alezheimer Disease NeuroImaging Initiative. High-throughput, fully automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment. Alzheimer Dis. Assoc. Disord. 23 , 139–145 (2009).

Ochs, A. L. et al. Comparison of automated brain volume measures obtained with NeuroQuant and FreeSurfer. J. Neuroimaging 25 , 721–727 (2015).

Ross, D. E. et al. Man versus machine Part 2: comparison of radiologists’ interpretations and NeuroQuant measures of brain asymmetry and progressive atrophy in patients with traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 27 , 147–152 (2015).

Ross, D. E., Ochs, A. L., Seabaugh, J. M. & Shrader, C. R. and the Alzheimer Disease Neuroimaging Initiative Man versus machine: comparison of radiologists’ interpretations and NeuroQuant(R) volumetric analyses of brain MRIs in patients with traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 25 , 32–39 (2013).

de Boer, R. et al. White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 45 , 1151–1161 (2009).

Vrooman, H. A. et al. Multi-spectral brain tissue segmentation using automatically trained k -nearest-neighbor classification. Neuroimage 37 , 71–81 (2007).

Romero, J. E., Coupe, P. & Manjon, J. V. HIPS: a new hippocampus subfield segmentation method. Neuroimage 163 , 286–295 (2017).

Coupe, P., Tourdias, T., Linck, P. Romero, J. & Manjon, J. LesionBrain: an online tool for white matter lesion segmentation. Lect. Notes Comput. Sci. , Springer 95–103 (2018).

Anjum, A. et al. Reusable services from the neuGRID project for grid-based health applications. Stud. Health Technol. Inf. 147 , 283–288 (2009).

Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17 , 162–173 (2018).

Harris, T. C., de Rooij, R. & Kuhl, E. The shrinking brain: cerebral atrophy following traumatic brain injury. Ann. Biomed. Eng. 47 , 1941–1959 (2019).

Thom, M. Review: hippocampal sclerosis in epilepsy: a neuropathology review. Neuropathol. Appl. Neurobiol. 40 , 520–543 (2014).

Maclaren, J., Han, Z., Vos, S. B., Fischbein, N. & Bammer, R. Reliability of brain volume measurements: a test–retest dataset. Sci. Data 1 , 140037 (2014).

Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15 , https://doi.org/10.1098/rsif.2017.0387 (2018).

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015).

Crossley, N. A., Scott, J., Ellison-Wright, I. & Mechelli, A. Neuroimaging distinction between neurological and psychiatric disorders. Br. J. Psychiatry 207 , 429–434 (2015).

Vita, A., De Peri, L., Deste, G. & Sacchetti, A. Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Transl. Psychiatry 2 , e190 (2012).

Grieve, S. M., Korgaonkar, M. S., Koslow, S. H., Gordon, E. & Williams, L. M. Widespread reductions in gray matter volume in depression. Neuroimage Clin. 3 , 332–339 (2013).

O’Doherty, D. C. M. et al. Frontal and subcortical grey matter reductions in PTSD. Psychiatry Res. Neuroimaging 266 , 1–9 (2017).

Wardenaar, K. J. & de Jonge, P. Diagnostic heterogeneity in psychiatry: towards an empirical solution. BMC Med. 11 , 201 (2013).

Lam, B., Masellis, M., Freedman, M., Stuss, D. T. & Black, S. E. Clinical, imaging, and pathological heterogeneity of the Alzheimer’s disease syndrome. Alzheimers Res. Ther. 5 , 1 (2013).

Logroscino, G. Classifying change and heterogeneity in amyotrophic lateral sclerosis. Lancet Neurol. 15 , 1111–1112 (2016).

Brugger, S. P. & Howes, O. D. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry 74 , 1104–1111 (2017).

Lei, D. et al. Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics. Psychol. Med . 1–10 https://doi.org/10.1017/S0033291719001934 (2019).

Alnaes, D. et al. Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry https://doi.org/10.1001/jamapsychiatry.2019.0257 (2019).

Pinaya, W. H. L., Mechelli, A. & Sato, J. R. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study. Hum. Brain Mapp. 40 , 944–954 (2019).

Vieira, S., Pinaya, W. H. & Mechelli, A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74 , 58–75 (2017).

Boschloo, L. et al. The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE 10 , e0137621 (2015).

Stampfli, P. et al. Subtle white matter alterations in schizophrenia identified with a new measure of fiber density. Sci. Rep. 9 , 4636 (2019).

Holmes, A. J. & Patrick, L. M. The myth of optimality in clinical neuroscience. Trends Cogn. Sci. 22 , 241–257 (2018).

Janssen, R. J., Mourao-Miranda, J. & Schnack, H. G. Making individual prognoses in psychiatry using neuroimaging and machine learning. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3 , 798–808 (2018).

Nunes, A. et al. Using structural MRI to identify bipolar disorders—13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0228-9 (2018).

Fortin, J. P. et al. Removing inter-subject technical variability in magnetic resonance imaging studies. Neuroimage 132 , 198–212 (2016).

Shinohara, R. T. et al. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin. 6 , 9–19 (2014).

Mechelli, A. et al. Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch. Gen. Psychiatry 68 , 489–495 (2011).

Palaniyappan, L. et al. Cortical folding defects as markers of poor treatment response in first-episode psychosis. JAMA Psychiatry 70 , 1031–1040 (2013).

Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137 , 2382–2395 (2014).

de Lange, S. C. et al. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat. Hum. Behav . https://doi.org/10.1038/s41562-019-0659-6 (2019).

Durstewitz, D., Koppe, G. & Meyer-Lindenberg, A. Deep neural networks in psychiatry. Mol. Psychiatry https://doi.org/10.1038/s41380-019-0365-9 (2019).

Article   PubMed   Google Scholar  

Bzdok, D. & Meyer-Lindenberg, A. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3 , 223–230 (2018).

Fuchs, E. & Flugge, G. Adult neuroplasticity: more than 40 years of research. Neural Plast. 2014 , 541870 (2014).

Scarpazza, C., Ferracuti, S., Miolla, A. & Sartori, G. The charm of structural neuroimaging in insanity evaluations: guidelines to avoid misinterpretation of the findings. Transl. Psychiatry 8 , 227 (2018).

Schmidt, P. et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59 , 3774–3783 (2012).

Shiee, N. et al. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49 , 1524–1535 (2010).

Ikram, M. A. et al. The Rotterdam Scan Study: design update 2016 and main findings. Eur. J. Epidemiol. 30 , 1299–1315 (2015).

Manjon, J. V., Tohka, J. & Robles, M. Improved estimates of partial volume coefficients from noisy brain MRI using spatial context. Neuroimage 53 , 480–490 (2010).

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Acknowledgements

This work was supported by a Wellcome Trust’s Innovator Award (208519/Z/17/Z) to A.M. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art. 1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua.

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Scarpazza, C., Ha, M., Baecker, L. et al. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 10 , 107 (2020). https://doi.org/10.1038/s41398-020-0798-6

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Taking RNAi from interesting science to impactful new treatments

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There are many hurdles to clear before a research discovery becomes a life-changing treatment for patients. That’s especially true when the treatments being developed represent an entirely new class of medicines. But overcoming those obstacles can revolutionize our ability to treat diseases.

Few companies exemplify that process better than Alnylam Pharmaceuticals. Alnylam was founded by a group of MIT-affiliated researchers who believed in the promise of a technology — RNA interference, or RNAi.

The researchers had done foundational work to understand how RNAi, which is a naturally occurring process, works to silence genes through the degradation of messenger RNA. But it was their decision to found Alnylam in 2002 that attracted the funding and expertise necessary to turn their discoveries into a new class of medicines. Since that decision, Alnylam has made remarkable progress taking RNAi from an interesting scientific discovery to an impactful new treatment pathway.

Today Alnylam has five medicines approved by the U.S. Food and Drug Administration (one Alnylam-discovered RNAi therapeutic is licensed to Novartis) and a rapidly expanding clinical pipeline. The company’s approved medicines are for debilitating, sometimes fatal conditions that many patients have grappled with for decades with few other options.

The company estimates its treatments helped more than 5,000 patients in 2023 alone. Behind that number are patient stories that illustrate how Alnylam has changed lives. A mother of three says Alnylam’s treatments helped her take back control of her life after being bed-ridden with attacks associated with the rare genetic disease acute intermittent porphyria (AIP). Another patient reported that one of the company’s treatments helped her attend her daughter’s wedding. A third patient, who had left college due to frequent AIP attacks, was able to return to school.

These days Alnylam is not the only company developing RNAi-based medicines. But it is still a pioneer in the field, and the company’s founders — MIT Institute Professor Phil Sharp, Professor David Bartel, Professor Emeritus Paul Schimmel, and former MIT postdocs Thomas Tuschl and Phillip Zamore — see Alnylam as a champion for the field more broadly.

“Alnylam has published more than 250 scientific papers over 20 years,” says Sharp, who currently serves as chair of Alnylam’s scientific advisory board. “Not only did we do the science, not only did we translate it to benefit patients, but we also described every step. We established this as a modality to treat patients, and I’m very proud of that record.”

Pioneering RNAi development

MIT’s involvement in RNAi dates back to its discovery. Before Andrew Fire PhD ’83 shared a Nobel Prize for the discovery of RNAi in 1998, he worked on understanding how DNA was transcribed into RNA, as a graduate student in Sharp’s lab.

After leaving MIT, Fire and collaborators showed that double-stranded RNA could be used to silence specific genes in worms. But the biochemical mechanisms that allowed double-stranded RNA to work were unknown until MIT professors Sharp, Bartel, and Ruth Lehmann, along with Zamore and Tuschl, published foundational papers explaining the process. The researchers developed a system for studying RNAi and showed how RNAi can be controlled using different genetic sequences. Soon after Tuschl left MIT, he showed that a similar process could also be used to silence specific genes in human cells, opening up a new frontier in studying genes and ultimately treating diseases.

“Tom showed you could synthesize these small RNAs, transfect them into cells, and get a very specific knockdown of the gene that corresponded to that the small RNAs,” Bartel explains. “That discovery transformed biological research. The ability to specifically knockdown a mammalian gene was huge. You could suddenly study the function of any gene you were interested in by knocking it down and seeing what happens. … The research community immediately started using that approach to study the function of their favorite genes in mammalian cells.”

Beyond illuminating gene function, another application came to mind.

“Because almost all diseases are related to genes, could we take these small RNAs and silence genes to treat patients?” Sharp remembers wondering.

To answer the question, the researchers founded Alnylam in 2002. (They recruited Schimmel, a biotech veteran, around the same time.) But there was a lot of work to be done before the technology could be tried in patients. The main challenge was getting RNAi into the cytoplasm of the patients’ cells.

“Through work in Dave Bartel and Phil Sharp's lab, among others, it became evident that to make RNAi into therapies, there were three problems to solve: delivery, delivery, and delivery,” says Alnylam Chief Scientific Officer Kevin Fitzgerald, who has been with the company since 2005.

Early on, Alnylam collaborated with MIT drug delivery expert and Institute Professor Bob Langer. Eventually, Alnylam developed the first lipid nanoparticles (LNPs) that could be used to encase RNA and deliver it into patient cells. LNPs were later used in the mRNA vaccines for Covid-19.

“Alnylam has invested over 20 years and more than $4 billion in RNAi to develop these new therapeutics,” Sharp says. “That is the means by which innovations can be translated to the benefit of society.”

From scientific breakthrough to patient bedside

Alnylam received its first FDA approval in 2018 for treatment of the polyneuropathy of hereditary transthyretin-mediated amyloidosis, a rare and fatal disease. It doubled as the first RNAi therapeutic to reach the market and the first drug approved to treat that condition in the United States.

“What I keep in mind is, at the end of the day for certain patients, two months is everything,” Fitzgerald says. “The diseases that we’re trying to treat progress month by month, day by day, and patients can get to a point where nothing is helping them. If you can move their disease by a stage, that’s huge.”

Since that first treatment, Alnylam has updated its RNAi delivery system — including by conjugating small interfering RNAs to molecules that help them gain entry to cells — and earned approvals to treat other rare genetic diseases along with high cholesterol (the treatment licensed to Novartis). All of those treatments primarily work by silencing genes that encode for the production of proteins in the liver, which has proven to be the easiest place to deliver RNAi molecules. But Alnylam’s team is confident they can deliver RNAi to other areas of the body, which would unlock a new world of treatment possibilities. The company has reported promising early results in the central nervous system and says a phase one study last year was the first RNAi therapeutic to demonstrate gene silencing in the human brain.

“There’s a lot of work being done at Alnylam and other companies to deliver these RNAis to other tissues: muscles, immune cells, lung cells, etc.,” Sharp says. “But to me the most interesting application is delivery to the brain. We think we have a therapeutic modality that can very specifically control the activity of certain genes in the nervous system. I think that’s extraordinarily important, for diseases from Alzheimer’s to schizophrenia and depression.”

The central nervous system work is particularly significant for Fitzgerald, who watched his father struggle with Parkinson’s.

“Our goal is to be in every organ in the human body, and then combinations of organs, and then combinations of targets within individual organs, and then combinations of targets within multi-organs,” Fitzgerald says. “We’re really at the very beginning of what this technology is going do for human health.”

It’s an exciting time for the RNAi scientific community, including many who continue to study it at MIT. Still, Alnylam will need to continue executing in its drug development efforts to deliver on that promise and help an expanding pool of patients.

“I think this is a real frontier,” Sharp says. “There’s major therapeutic need, and I think this technology could have a huge impact. But we have to prove it. That’s why Alnylam exists: to pursue new science that unlocks new possibilities and discover if they can be made to work. That, of course, also why MIT is here: to improve lives.”

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HUGE EXPECTATIONS AS INTERNATIONAL INNOVATION CONFERENCE KICKS OFF IN NAIROBI

The Third Multisectoral Conference and Exhibition on Research, Science, Technology and Innovation (MS-CEORSTI) that kicked off in Nairobi on Tuesday will end tomorrow, a deliberation that focused primarily on using technology to better lives.

The yearly conference, which is organized by National Commission For Science, Technology and Innovation (NACOSTI) and 13 other consortiums, aims to facilitate a multifaceted forum of national and international discourse to deliberate, network, partner, share experiences and resolve on how best to infuse or deploy Science, technology and innovation for prosperity of humanity as well as for public good, safety and security.

The conference has attracted experts, professionals and researchers from around the world on a technology-driven and innovation-led inclusive sustainable development agenda.

Prof Walter Oyawa, Director General NACOSTI, said Science, Technology and Innovation (STI) have direct impact on the economy of nations and countries must invest in it.

He noted that the government has made matters of STI priority in addressing issues facing communities nationally and globally.

Deputy Head of Public Service Josephat Nanok, who was the chief guest, said the conference is timely and   STI contributes to national production.

Nanok said the national government is applying STI for national prosperity and national good, being a key enabler of the five strategic pillars that constitute the bottom up transformation agenda.

He noted that the government supports young people to innovate to improve service delivery in all sectors of the economy.

The council will give information on relevance of STI to national security, public safety, public health, food and national security, climate change mitigation, and inclusive sustainable development.

Principal Secretary (PS) in the State Department for Higher Education and Research Dr Beatrice Inyangala, who represented Education CS Ezekiel Machogu, pointed out that the ministry supports STI by continuing to apply it in all government programmes to improve Kenya’s competitiveness and to unlock its full potential.

She noted that International Science Council governing board led by Prof Peter Gluckman is also holding a crucial meeting in Nairobi, which she noted will see it increasing its presence in Africa.

The agencies that partnered with NACOSTI to convene the conference include National Research Fund ( NRF), Kenya National Innovation Agency (KENIA),National Counter Terrorism Centre (NCTC), Centurion Systems Limited, Konza Technopolis, National Defence University (NDU), Kenya Industrial Property Institute (KIPI), Egerton University, Kenya Vision 2030 Delivery Secretariat (VDS), Kenya Association of Manufacturers (KAM), Open University of Kenya (OUK), Engineers Board of Kenya (EBK), and The Kenya Advanced Institute of Science and Technology (Kenya -AIST).

Source: https://www.educationnews.co.ke/huge-expectations-as-international-innovation-conference-kicks-off-in-nairobi/

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Minister Wilkinson Announces Progress on Canada’s Hydrogen Strategy

From: Natural Resources Canada

News release

The Honourable Jonathan Wilkinson, Minister of Energy and Natural Resources, launched the Hydrogen Strategy for Canada: Progress Report. The Report is the result of three years of engagement, research and analysis, including with over 1,000 experts and stakeholders. It highlights the significant investments and notable developments that have advanced Canada’s hydrogen sector since 2020, features projections of hydrogen’s potential role in meeting Canada’s climate objectives and provides a roadmap for next steps and priorities.

May 10, 2024                            Ottawa, Ontario           Natural Resources Canada

As we continue to foster economic growth and build Canada’s clean economy, significant investments and progress are being made in the production, distribution, and use of clean fuels, including low-carbon hydrogen. These advancements are guided by Canada’s Hydrogen Strategy, which introduced a framework in 2020 to help position Canada as a global supplier and producer of low-carbon hydrogen on our path to net-zero by 2050.

Today, the Honourable Jonathan Wilkinson, Minister of Energy and Natural Resources, launched the Hydrogen Strategy for Canada: Progress Report . The Report is the result of three years of engagement, research and analysis, including with over 1,000 experts and stakeholders. It highlights the significant investments and notable developments that have advanced Canada’s hydrogen sector since 2020, features projections of hydrogen’s potential role in meeting Canada’s climate objectives and provides a roadmap for next steps and priorities. 

Interest in low-carbon hydrogen in Canada has increased significantly in recent years, with over 80 low-carbon hydrogen production projects currently in various stages of development, representing an economic opportunity of over $100 billion in potential investment in domestic clean energy opportunities and jobs.  

This comes as Budget 2024 presents key investments to incentivize the development and adoption of clean fuels and accelerate innovation and investments in Canadian hydrogen, including the implementation of the Clean Hydrogen Investment Tax Credit and other major economic investment tax credits.

The federal government will continue to advance domestic low-carbon hydrogen opportunities to position Canada as a competitive and reliable supplier in the growing global market for clean fuels. 

“Unlocking the potential of clean hydrogen is an essential step to position Canada as a global leader of clean renewable fuels. Today’s Progress Update testifies to the remarkable advancements made in the hydrogen landscape since we released our Hydrogen Strategy in 2020, underscoring our dedication to sustainable energy innovation in the race to net zero.” The Honourable Jonathan Wilkinson Minister of Energy and Natural Resources

Quick facts

Since 2020, British Columbia, Alberta, Ontario, Quebec, Nova Scotia, and New Brunswick have published hydrogen strategies, identifying hydrogen as a provincial clean energy priority and describing provincial actions and objectives to realize regional low-carbon hydrogen objectives.

Canada has signed 12 international agreements to provide energy security and advance clean hydrogen export opportunities, including Germany, the Netherlands, the United States, South Korea and Japan.

There are now 13 low-carbon hydrogen production facilities in operation across Canada, able to produce over 3,000 tonnes of low-carbon hydrogen per year.

Associated links

• Hydrogen Strategy for Canada and Progress Report
• Budget 2024: Fairness for Every Generation
• Budget 2024 – Attracting Investment for a Net-Zero Economy
• Backgrounder: Economic Growth and Productivity
• 2030 Emissions Reduction Plan: Clean Air, Strong Economy
• Net-zero emissions by 2050
• Energy Innovation Program

Natural Resources Canada Media Relations 343-292-6100 [email protected]

Carolyn Svonkin Press Secretary Office of the Minister of Energy Natural Resources 343-597-1725 [email protected]

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