job satisfaction questionnaire thesis

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• v.27(6); 2020 Dec

The Development and Validation of Job Satisfaction Questionnaire for Health Workforce

Nurul fatma diyana ahmad.

1 Medical Division, Sarawak State Health Department, Ministry of Health, Sarawak, Malaysia

Alex Kim Ren Jye

2 Quality Unit, Sarawak General Hospital, Sarawak, Malaysia

Zubalqiah Zulkifli

Mohamad adam bujang.

3 Clinical Research Centre, Sarawak General Hospital, Sarawak, Malaysia

This study aims to develop and validate a job satisfaction questionnaire (JS-Q) for health workforce who are employed by a healthcare institution.

The study consists of six phases which begins with eliciting a conceptual understanding of the subject matter which is then followed by questions development, designing the overall structure and format of the questionnaire, assessing both its content validity and face validity, conducting a pilot study and finally a field test. A sample of study respondents who were permanent hospital staff above 18 years of age had been recruited from three government hospitals in Kuching, Sarawak, Malaysia.

The finalised JS-Q consists of a total of 34 questions that were based on 8 domains. For all these 8 domains, the minimum loading of each item on the factors was calculated to be at least 0.500, its coefficient of Cronbach’s alpha was calculated to be at least 0.750 and its corrected item-total correlation was calculated to be at least 0.500. The goodness of fit of the model was determined to be satisfactory with a value of Chi-square/df < 3.0, and a value of root mean square error approximation (RMSEA) < 0.8 and finally with both Tucker Lewis index (TLI) and comparative fit index (CFI) > 0.9.

This newly developed and validated questionnaire (JS-Q) is found to be a valid and reliable study instrument for assessing job satisfaction among health workforce.

Introduction

The healthcare industry increasingly requires a skilled workforce due to rapid advancements in medical technology, in concert with an ever-increasing expectation of patients towards more sophisticated methods for the optimal delivery of patient care. Job satisfaction among healthcare employees is essential for attracting and retaining top performers within the healthcare industry, by improving staff morale within the organisation. This is because job dissatisfaction can be a major cause of absenteeism and turnover among healthcare employees, which can adversely affect employees’ organisational commitment and the quality of healthcare services rendered ( 1 – 2 ).

Job dissatisfaction can be a major cause for concern within a knowledge-based sector such as the healthcare industry. In fact, such a heavy emphasis being placed on job satisfaction for the employees from the knowledge-based sectors (for example, the healthcare industry) shows that it is as important in these sectors as in the other business sectors. This is particularly true for the professional and service-based organisations such as hospitals, where the provision of long-term specialist training for medical professionals and retaining these highly skilled healthcare staff are deemed highly important ( 1 ).

This study aims to develop a new job satisfaction questionnaire (JS-Q) for health workforce. Although many existing varieties of JS-Q are already widely available in the literature, our intention for this study is to develop this questionnaire and to tailor its psychometric properties to be feasible for assessing job satisfaction among the entire local healthcare workers, in order to provide a mechanism for the continuous assessment and monitoring of their job satisfaction, especially within the local healthcare setting.

Job satisfaction serves an important function by enhancing the level of employee’s motivation and productivity ( 2 – 3 ). It is also an important measure which enables the top management and policymakers to constantly monitor the level of job achievement, so that they can seek various means to upgrade their job management and job enhancement. Without proper monitoring of job satisfaction, it is possible for employee behaviour to have a negative impact on their working environment and subsequently their productivity ( 4 ). Although the term ‘job satisfaction questionnaire’ is usually regarded as a common study instrument, it is however still necessary to revisit the specific domains and items for the measurement of job satisfaction, considering a myriad of challenges that are currently faced by these employees in their working environment nowadays, especially in the healthcare sector.

Hence, this newly developed questionnaire will prove very useful for providing continuous feedback to the top management as well as healthcare policymakers of the medical institutions on the level of job satisfaction reported by the health workforce from time to time. Such a feedback provided by the existing health workforce will immediately alert them about any adverse working conditions that present as factors which result in job dissatisfaction among these employees. This will hopefully prompt the authorities to identify the root cause of job dissatisfaction, in order to take all the necessary steps to address it by increasing their job satisfaction as well as making sure they are being heard with regard to their employment needs.

Population and Sample

In this study, the study population refers to all the healthcare employees who are currently working in Sarawak, Malaysia. A sample of study respondents was recruited from three government hospitals in Kuching, Sarawak which include Sarawak General Hospital, Bau Hospital and Lundu Hospital. All these study respondents recruited for this study were permanent hospital staff of above 18 years of age.

Data Collection

The study respondents were recruited by using the snowball sampling technique, which involved the use of their email addresses. The researchers sent an email to each study participant which provided a link to the google form of the JS-Q for him/her to fill in.

Process of Questionnaire Development

The whole process of developing this questionnaire has been summarised in Figure 1 . It involved a total of six phases starting from an initial exploration of the subject matter which encompassed the overall conceptualisation of the underpinning theory of job satisfaction. This was also an observational study that utilised both a qualitative approach (i.e. to study the background subject matter, to develop the specific items of the questionnaire, to determine both the structure and format of questionnaire and finally to assess its content & face validity) and a quantitative approach (i.e. to conduct a pilot study and a field test) to develop the scale for the measurement of job satisfaction.

The recommended process for questionnaire development

This research adopted the conventional framework for the development of measurement scales in which all the authors begin by taking the first step to conduct an extensive literature review of job satisfaction to enhance their understanding of the subject matter by exploration before developing the conceptual framework for this JS-Q, as shown in Figure 2 ( 1 – 9 ). Upon identification of a list of core components of the concept of ‘job satisfaction’, the authors then proceeded to develop the items of this JS-Q which were further categorised in 7 different domains. Hence, the first draft of the questionnaire was initially prepared, which included a total of 43 items within 7 domains namely i) ‘Empowerment and participation’ with 6 questions; ii) ‘Working condition’ with 8 questions; iii) ‘Reward and recognition’ with 6 questions; iv) ‘Teamwork’ with 5 questions; v) ‘Training and Development’ with 5 questions; vi) ‘Communication’ with 7 questions and vii) ‘Leadership’ with 6 questions.

The conceptual framework for JS-Q

Its content validity was assessed by three subject matter experts (SMEs) and this panel of SMEs included a high-ranking medical officer, an administrative officer providing diplomatic services who was holding a Master’s degree in human resource and a research consultant holding an MBA qualification. The final draft of the questionnaire was then pre-tested among 10 healthcare workers to determine its face validity. Its face validity was found to be satisfactory with only a few minor modifications necessary. Next, a total of 30 healthcare workers were then recruited for conducting a pilot test of this JS-Q, from which the reliability of this JS-Q was assessed to be satisfactory, as measured by its high value of internal consistency (Cronbach alpha > 0.800).

Finally, this questionnaire was field-tested within 1 month’s time from April 2019 until May 2019, before they were used in the main study. This field-testing of JS-Q involved conducting a cross-sectional survey among a group of study respondents who were permanent healthcare workers recruited from three hospitals in Kuching, Sarawak. In this survey, each study respondent was required to fill in a self-administered questionnaire that consists of questions on each individual’s socio-demographic profile and his/her job satisfaction level.

Statistical Analysis

The ultimate aim of this study was to develop a new questionnaire that can accurately assess job satisfaction. Descriptive statistics were initially used to describe the socio-demographic profiles of all the study respondents. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were then conducted to determine the construct validity which then led to the selection of the best construct in this JS-Q for assessing the job satisfaction level. In order to verify the construct validity (which comprises both discriminant and convergent validity), the EFA was conducted by utilizing principle component analysis (PCA) with the varimax rotation method, which had applied an Eigenvalue of > 1 for this purpose. During this process, it is necessary to delete any items with either a cross loading of more than 0.40 or a loading of less than 0.40.

After using EFA to identify the factor structure present in a set of variables, the model fit was then assessed by using CFA where indicators such as Tucker Lewis Index (TLI ≥ 0.90), Comparative Fit Index (CFI ≥ 0.90), root mean square error approximation (RMSEA ≤ 0.08) and Akaike information criterion (AIC) were estimated ( 10 – 11 ). Deletion of items in the questionnaire was performed in a consecutive manner, and an item would be deleted if its content validity was too low and/or there were some abnormally large values associated with their error covariances from among the various items. This process of using CFA to test a hypothesised factor structure or model by assessing its goodness of fit to the data would continue until a satisfactory model fit has been achieved.

Sample Size Planning

The determination of minimum required sample size was based on a rule-of-thumb for EFA since EFA has been applied to test the validity of the JS-Q. Initially, it was estimated that 40 items had been developed for the JS-Q. This means that based on the rule of thumb of using 5:1 ratio for sample size determination, the minimum sample size of 40 × 5 = 200 study participants would be required for using the EFA for this questionnaire development study ( 12 – 14 ).

Baseline Sociodemographic Profile of the Respondents

A total of 343 study respondents were participating in this study, with a majority being female (74.8%) whose age ranges from 35 to 44 years (39.4%). More than three-quarters of these respondents are married (79.7%). Half of them are Bidayuh (50%). The largest proportion of these respondents are from the Medical Service Department (55.3%). Nearly half of them are nurses or community nurses (42.1%), whose job grades range from 27–40 (43.7%). The highest number of respondents consists of those who had worked in the hospital for more than 10 years (43.9%) whose monthly salary ranges from RM3,000 to less than RM5,000 (43.9%) ( Table 1 ).

Characteristics of respondents from government healthcare workers in Kuching division

Job Satisfaction Questionnaire

After conducting EFA using the PCA with varimax rotation method, which extracted those factors with Eigenvalues > 1, the proposed JS-Q questionnaire had been designed to consist of 39 questions that were categorised in 8 domains, namely: i) Leadership (8 questions); ii) Training and development (5 questions); iii) Teamwork (7 questions); iv) Empowerment and participation (5 questions); v) Working condition (3 questions) and vi) Reward and recognition (5 questions); vii) Communication (3 questions) and viii) Flexibility of working hours (3 questions). The domain of ‘Working condition’ was split into 2 sub-domains, namely: working condition and flexibility of working hours. All the items in the JS-Q were found to fit well within their respective domains, based on their content and also through determination by using relevant statistical computations. For all the domains of JS-Q, the minimum factor loading for each item was 0.500, the value of coefficient of Cronbach’s alpha was at least 0.850 and its corrected item-total correlation was at least 0.600.

The construct which was developed by using EFA was later re-examined using CFA to determine its model fit. The Chi-square/ df was 2.739 with RMSEA was 0.067 although both TLI and CFI were slightly lower than 0.90 ( Table 2 ). Five items were deleted because they did not demonstrate adequate content validity (since they failed to result in measures that will adequately sample the theoretical domain of interest) and also there was an excessive amount of covariance error terms (as suggested by the modification indices of the measurement model). These five items were deleted one by one until the model reached a satisfactory model fit.

Model fit indices of JS-Q 39 items versus JS-Q 34 items

The final version of the JS-Q now consists of only 34 items that were categorised in 8 domains, namely: i) Leadership (5 questions); ii) Training and development (5 questions); iii) Teamwork (5 questions); iv) Empowerment and participation (5 questions); v) Working conditions (3 questions); vi) Reward and recognition (5 questions); vii) Communication (3 questions) and viii) Flexibility of working hours (3 questions). Several indicators of the goodness of fit of the model were found to be satisfactory with Chi-square/ df < 3.0, RMSEA = 0.06, both TLI and CFI were higher than 0.9 and also its AIC = 1467.8 was found lower than that of the original version of JS-Q which consists of 39 questions (AIC = 2151.1) ( Table 2 ).

Next, the construct of the final version of JS-Q was tested by using EFA which revealed that the EFA factor solution did not adequately incorporate all the total 8 domains into the conceptual framework of JS-Q. However, when EFA was conducted on the same subset of participants for determining the factor structure of the 34-items of the final version of JS-Q, and such factor analyses were repeated until a solution in which all the items included in the analysis had met all these criteria was obtained, it was again found that the same 8 domains together with their respective items would then be incorporated into the conceptual framework of JS-Q after the EFA had successfully extracted an 8-factor solution in a 34-item measure. For all these 8 domains extracted by EFA, the minimum factor loading for each item was 0.500, the value of coefficient of Cronbach’s alpha was at least 0.750 and its corrected item-total correlation was at least 0.500 ( Table 3 ).

Result of EFA and internal consistency for JS-Q which consists of 34 items and 8 domains

Notes: Model fit indices based on CFA are described in Table 1 ; EFA was conducted based on PCA using the varimax rotation method and and the factor solution is forced into eight domains; TW = Teamwork; LD = Leadership; RR = Rewards and recognitions; EP = Empowerment; TD = Training and development; WH = Flexibility of working hours; C = Communication; WC = Working condition

The JS-Q was initially developed and validated as a new, self-administered instrument for measuring job satisfaction among all the hospital staff. The final construct of the JS-Q has now been designed to consist of a total of 8 domains with 34 items, along with an additional domain (namely, flexibility of working hours) being incorporated into the original conceptual framework of the study ( Figure 2 ). Thus, the conceptual framework of this JS-Q is now based on 8 domains, namely: i) Leadership; ii) Training and development; iii) Teamwork; iv) Empowerment and participation; v) Reward and recognition; vi) Communication; vii) Working conditions and viii) Flexibility of working hours.

It is likely for both leadership style and organisational culture to have a positive influence on an employee’s level of job satisfaction, especially when the leaders have a vision which is aligned with their organisational culture, which typically occurs within the framework of a transformational leadership. As a result, both employees and their superiors will cooperate not only for the sake of organisation’s well-being, but also for the fulfilment of their personal needs and desires ( 7 ). Meanwhile, the provision of in-house training for human resource development will provide numerous benefits to an organisation, especially in relation to preventing errors, improving workplace safety and decreasing staff turnover. It may also be helpful for an organisation to cultivate a learning environment, which may promote innovation and impart a friendlier and more conducive organisational culture. To do so, it is necessary to specifically allocate adequate financial resources for expanding such efforts ( 9 ).

Executive leaders and managers should reinforce the importance of teamwork among all employees because effective teamwork will enable them to align themselves towards a common goal, thereby enhancing employees’ motivation and a sense of belonging, which will indirectly boost their level of job satisfaction. By doing so, the employees will also be motivated to accomplish a mutual goal ( 7 ). On the other hand, the empowerment of employees and their participation should first be initiated by the top management. To do so, it involves moving the capacity for decision-making to the lowest possible level within the organisation. Hence, the employers will empower all the employees by strongly encouraging them to open up by meeting together to discuss any matters arising from their work, as well as any other matters arising from their job functions, whether it involves managerial decisions and/or high-level policies ( 7 ). Besides that, organisations will also have to develop a formal reward and recognition system to enlist the support of employees, by engaging them in teamwork. In order to ensure that due recognition will be given to any significant contribution and/or effort made by an employee, all the departments of an organisation should also be rewarded as a gesture to promote and support the attainment of a particular performance level as a common goal within the whole organisation ( 8 ).

Communication is a basic tool that is used at all levels of the working environment as well as between the top management and the workforce at the ground level. Managers will be better equipped to foster job satisfaction and effective organisational commitment through a proper channel for internal communication wherein they recognise and acknowledge all the input from the employees. Managers must have a clear understanding of both the quantity and quality of information sought by the employees if they intend to design an effective two-way internal communication channel that meets the information needs of an employee ( 5 ). By doing so, all the employees will regularly be provided with information from the management from time to time, and are also given an opportunity to be heard by the management; thereby both the employees and the management staff will have a closer knit of working relationship together.

The initial items for specifying the concept of ‘working condition’ in JS-Q was split into two, namely ‘working condition’ and ‘flexibility of working hours’. The original conceptual framework of the construct of JS-Q had defined the term ‘working condition’ as ‘the condition under which a job has been performed’. This can be influenced by i) external factors that include climate; ii) subjective factors that include fatigue, monotony and unfavourable posture etc; iii) factors related to the organisation of production such as duration of work shift, design of work schedule and duration of working time, etc. ( 6 ).

It is commonly known that the working condition can have a significant effect on job satisfaction because it can influence the quality of the physical environment where they would be working. Therefore, the term ‘working condition’ can include many aspects of the working environment such as adequate workspace, appropriate level of lighting, minimal noise level, thermal comfort working area, the provision of essential utilities such as electricity and water supply, and the availability of office equipment. Management should also provide ergonomically-designed workspaces that enhance employees’ health and well-being ( 4 ). Flexibility of working hours is now becoming increasingly important among the workers. Many organisations now begin to offer flexible working hours to employees due to the potential benefits that such a flexibility can bring to both the employers and their employees. Examples of these benefits include a higher level of employee productivity along with a higher level of organisational profitability. Most importantly, the option of flexible working hours can also promote and maintain work-life balance among the workforce, which is an important aspect of a healthy work environment ( 15 ).

The existing literature on the many different facets of ‘job satisfaction’ have all supported the construct of all the expected 8 domains of a job satisfaction measure in JS-Q. In addition, the evidence of reliability and validity of the scales in JS-Q was also provided by both EFA and CFA. Although the re-analysis of the final construct of JS-Q by using the EFA with the added criterion of having an eigenvalue of > 1 for retaining the items for an ideal construct (in order to improve the overall model fit) did not manage to incorporate all the expected 8 distinct domains into the conceptual framework of the construct of this questionnaire, however we can nevertheless still be confidently using EFA to successfully extract an 8-factor solution in a 34-item measure by incorporating all the 8 domains together with each of their respective items into the conceptual framework of the construct of this JS-Q. This was done by conducting EFA on the same subset of participants to determine the factor structure of this questionnaire and these factor analyses were repeated until a factor solution which incorporated all the items that had fulfilled the criteria was identified. This justifies why the authors should rightfully insist on maintaining the existing 8 domains since they have already provided a good reflection of its conceptual framework which had been developed previously. Most importantly, all the model fit indices of the final construct of this JS-Q (consisting of 34 items) were found to be satisfactory, which means that the final construct of the questionnaire has a better model fit than the previous construct of JS-Q (consisting of 39 items).

This also explains why if the researchers conducted the EFA by using the criterion of having an eigenvalue of > 1 for retaining the items for an ideal construct, but did not manage to identify a construct consisting of 8 domains in the final construct of 34-item measure of JS-Q; then it will be strongly recommended for the researchers to conduct EFA again on the same subset of participants to determine the factor structure of this questionnaire, and then to repeat all the factor analyses until a factor solution in which all the items that have fulfilled all criteria are now included in the analysis. This is an important and necessary step for the researchers to identify a model specification that forces all the 8 domains into the factor solution during the factor analysis. In addition, as long as the internal consistency for each domain of the construct is found to be satisfactorily high (i.e. > 0.65) and the model fit was assessed to be satisfactory by CFA, then the construct of JS-Q can be considered a reliable and valid study instrument for measuring job satisfaction.

A major strength of this study is that its findings are demonstrated to have relevance in many important practical and theoretical applications. Apart from its use for measuring job satisfaction among the internal workforce within the organisation and also for research purposes, this study has identified a new independent domain which can be used to assess the job satisfaction dimension, namely flexible working hours. Moreover, this study has also determined the high significance of this new domain which is then subsequently incorporated it into the final construct of the JS-Q.

Another major achievement of this study for the validation of this JS-Q is that both its reliability and validity were found to be satisfactory, which was substantiated by the appropriate selection of the items for each of the respective domains together with an adequate sample size of study respondents who were recruited for this study. This study had recruited a total of 343 respondents and hence it is clear that this sample size has greatly exceeded the minimum sample size required for both EFA and CFA ( 12 – 14 ).

However, an important limitation of this study is that the authors of this study are well aware of the fact that although this JS-Q has been validated among healthcare workers, its psychometric properties (i.e. assessment of its validity and reliability) remain untested in workers of other fields. We therefore encourage the JS-Q to be validated in different languages as well as to apply it within various other types of organisations. For example, it is possible to replace the term ‘hospital’ with another term such as ‘healthcare organisation’ or ‘healthcare department’ so that the JS-Q can then potentially be applied in other types of organisational settings.

Insofar that it is the authors’ intention to generalise the applicability of this JS-Q to a broader range of study respondents, however it must still be noted that further studies are required to confirm its reliability and validity as a research instrument for use in workers of other fields since this study had recruited only healthcare workers as its study respondents. Therefore, it may not be valid for the results obtained from this study to be extrapolated to other study settings and/or study respondents apart from the healthcare industry.

The JS-Q is found to be both a valid and reliable study instrument for measuring job satisfaction among the healthcare workers and can also be recommended for use in several other related purposes, such as in management and research that involve an assessment of job satisfaction. Hence, it is now recommended for the JS-Q to be used for measuring job satisfaction among healthcare workers. Lastly, the authors also strongly recommend the readers to apply the items contained in the JS-Q 34 items ( Appendix 1 ) for use as a research instrument to measure job satisfaction among employees in many different organisational settings other than the healthcare sector, in which it had originally been developed and validated, as long as its psychometric properties have been fully tested and it has been found to be both a valid and reliable instrument prior to using it in all these settings.

Acknowledgements

We would like to thank the Director General of Health Malaysia for his permission to publish this article and Mr Hon Yoon Khee for proofreading this article. We also appreciate our officers who have helped us in the recruitment of respondents in respondents’ recruitment in Sarawak General Hospital, Bau Hospital and Lundu Hospital.

Appendix 1. 

Hospital internal client satisfaction survey/ kaji selidik tahap kepuasan kakitangan hospital.

Please read each statement carefully and determine the degree to which you agree with these statements below. Kindly take your organisation into consideration as you respond to each statement. Please mark only 1 answer for each statement.

Sila baca setiap penyataan di bawah dengan teliti dan tentukan tahap sejauh mana anda bersetuju terhadap penyataan berikut. Sila pertimbangkan organisasi anda dalam menjawab setiap penyataan. Sila tanda hanya 1 jawapan bagi setiap penyataan .

A. Teamwork (TW) / Kerja Berpasukan

B. leadership (ld) / kepimpinan, c. reward and recognition (rr) / ganjaran dan pengiktirafan, d. empowerment and participation (ep) / penguasaan dan penyertaan dalam organisasi, e. training and individual development (td) / latihan dan perkembangan individu, f. working hours (wh) / masa bekerja, g. communication (c) / komunikasi, h. working condition (wc) / persekitaran kerja.

Ethics of Study

Ethical approval for this study was obtained from the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (NMRR 18-3166-41968).

Conflict of Interest

Authors’ Contributions

Conception and design: NFDA, AKRJ, MAB

Analysis and interpretation of the data: NFDA, AKRJ, MAB, ZZ

Drafting of the article: NFDA, AKRJ, MAB

Critical revision of the article for important intellectual content: MAB

Final approval of the article: AKRJ, MAB, ZZ

Provision of study materials or patients: NFDA

Statistical expertise: MAB

Obtaining of funding: AKRJ

Administrative, technical, or logistic support: AKRJ

Collection and assembly of data: NFDA, ZZ

7 Best Job Satisfaction Scales, Questionnaires & Surveys

Sometimes we feel very satisfied, other times we feel incredibly disheartened and unsatisfied.

Measuring job satisfaction is important because it can predict our future behavior (Faragher, Cass, & Cooper, 2013).

For example:

• Are we likely to resign from our jobs?
• Are we at risk of poor health?
• Is it highly likely that we will suffer burnout?

In this post, we explore various ways of measuring job satisfaction. We’ll look at the most widely used tools in the literature and discuss other challenges of measuring job satisfaction.

Finally, we will look at the resources available at PositivePsychology.com to increase job satisfaction among employees.

Before you continue, we thought you might like to download our three Work & Career Coaching Exercises for free . These detailed, science-based exercises will help you or your clients identify opportunities for professional growth and create a more meaningful career.

This Article Contains:

5 best practices for measuring job satisfaction, 3 evidence-based questionnaires, 2 surveys and scales to measure employees’ satisfaction, 2 key metrics to consider, 2 tools for measuring employee engagement, a note on employee health: measuring stress and burnout in the workplace, increasing job satisfaction: 6 positivepsychology.com tools, a take-home message.

What is meant by the term ‘job satisfaction,’ and how is it measured?

Challenges when measuring job satisfaction

As a psychological construct, job satisfaction is meant to reflect employees’ level of satisfaction with their work.

Questionnaires that measure job satisfaction ask questions about various attitudes and behaviors; the responses to these questions are totaled and reflect job satisfaction. This implies that an employee might have low job satisfaction, but their score might be explained by low scores on only one dimension.

Furthermore, job satisfaction develops slowly. It is a dynamic process, and job satisfaction now does not guarantee job satisfaction in five years . This is because job satisfaction is affected by many conditions within the workplace, and these conditions can change.

Therefore, job satisfaction as a measurable psychological construct describes the attitude of the employee to the current workplace conditions (Earl, Minbashian, Sukijjakhamin, & Bright, 2011).

Five best practices

Knowing this, the best practices for measuring job satisfaction are as follows:

• Measure job satisfaction regularly so that you have a baseline measurement for each employee or can calculate an average across employees. With a baseline measurement on hand, you can track changes in job satisfaction.
• Using questionnaires and surveys is one of the multiple ways to track job satisfaction. The advantage of these tools is that employees can respond privately, without the added pressure of social interaction. However, keep in mind that these responses are still self-reported, and employees may report in ways that appear socially desirable.
• Follow up questionnaires and surveys with interviews and discussions. Check in regularly with employees, address grievances, and provide feedback . Regular meetings require time and effort, but personal check-ins are very useful and can help develop positive relationships with employees .
• Check in with superiors, team leads, and managers to discuss the engagement of team members .
• Provide a way for employees to report grievances anonymously. Steps 2 and 3 are not anonymous, and therefore some employees may not feel comfortable raising thorny issues. An anonymous process, like a suggestion box, gives employees an avenue to report sensitive issues.
• Assure employees that their responses are confidential and their responses will not be shared with anyone except the people scoring the questionnaires.

Van Saane, Sluiter, Verbeck, and Frings-Dresen (2003) evaluated 35 different tools that measure job satisfaction in a meta-analysis.

To be considered in the meta-analysis, the tools had to meet acceptable psychometric standards, including an internal reliability of 0.80 or higher, a test-retest coefficient of 0.70 or higher, and at least four measured work factors that were proposed to affect job satisfaction.

Although 29 items were included in the meta-analysis, only 7 met the criteria for reliability and validity. Of these, four items were developed for nurses and physicians. The remaining three tools were:

• The Job in General Scale (JIG) & Job Descriptive Index (JDI)
• The Job Satisfaction Survey (JSS)
• The Andrews and Withey Job Satisfaction Questionnaire

Job in General Scale & Job Descriptive Index

Of these three, the JIG Scale is one of the most well-used questionnaires to measure job satisfaction (Ironson, Smith, Brannick, Gibson, & Paul, 1989). The JIG was developed to accompany another worthwhile tool to measure job satisfaction: the JDI (Smith, Kendall, & Hulin, 1969).

Both tools can be administered together as a single tool. For these tools, employees select items that appropriately describe a particular aspect of their career. For example, employees must indicate if the item ‘Stimulating’ describes their colleagues, answering ‘yes,’ ‘no,’ or ‘cannot decide.’

The JDI and JIG are freely available, and the administrative manual, norms, and a scoring manual can be requested from the Bowling Green State University website .

Job Satisfaction Survey

Although the Job Satisfaction Survey (Spector, 1985) was designed to measure satisfaction among employees who work in the human service, public, and nonprofit sector organizations, Spector argues that the JSS applies to other industries as well. The JSS is also much shorter than other surveys, with 36 items in total.

Each item is a statement, and the employee must show their level of agreement on a scale from 1 to 6, where 1 indicates ‘disagree very much’ and 6 indicates ‘agree very much.’ The 36 items map onto 9 different dimensions, and responses to each subscale are summed. The items, administration, and scoring instructions can be found on Paul Spector’s website .

Andrews and Withey Job Satisfaction Questionnaire

The Andrews and Withey Job Satisfaction Questionnaire was developed in 1976 and is outlined in the book Social Indicators of Well-Being: Americans’ Perceptions of Life Quality (Andrews & Withey, 2012). The almost 100-page questionnaire must be purchased from the authors.

Although the questionnaire has satisfactory psychometric properties, the questionnaire is extremely long to administer.

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Let’s look at two surveys that can be used to measure employees’ job satisfaction.

The Gallup Workplace Audit

The Gallup Workplace Audit (GWA) measures various actionable aspects of the workplace, including work satisfaction (Gallup Organization, 1992–1999). In total, there are only 13 items. Employees respond on a scale from 1 to 5.

For the first item about workplace satisfaction, 1 indicates ‘extremely dissatisfied,’ and 5 indicates ‘extremely satisfied’; however, for the next 12 items, the anchors change to ‘extreme disagreement’ and ‘extreme agreement,’ respectively.

These 12 items comprise the Q 12 (Harter, Schmidt, Killham, & Agrawal, 2009), which has been used extensively and has good psychometric properties. Although the items of the GWA are listed in Harter, Schmidt, and Hayes (2002), the GWA may not be used without permission from The Gallup Organization .

Job Diagnostic Survey

The Job Diagnostic Survey (JDS) has been used across various research (Hackman & Oldham, 1974, 1975). The JDS measures overall job satisfaction and satisfaction for five dimensions of work, such as

• Job security
• Supervision
• Growth opportunities

The survey is split into eight sections, and in the fourth, the employee rates their level of satisfaction with the five dimensions of work. The survey takes less than 30 minutes to administer. The full scoring instructions are listed in Hackman and Oldham’s (1974) research paper, which is available from the ERIC Institute of Education Sciences website .

Moderating variables

The relationship between job satisfaction and job performance is complicated; job satisfaction influences job performance, which in turn, influences job satisfaction.

Furthermore, the relationship between job satisfaction and job performance is also influenced by multiple factors.

Specifically, the effect of job satisfaction on job performance can be heightened by several variables including:

• Personality/self-concept of the employee
• Autonomy of the employee
• The level of analysis used for the questionnaires

When job satisfaction is measured using a questionnaire with multiple dimensions, the correlations between each dimension and job performance are weaker than when composite job satisfaction is constructed from all the dimensions.

Furthermore, the effect of job performance on job satisfaction is also influenced by several variables including:

• Rewards for good job performance
• The nature of the job
• How important achievement is to the individual
• How important work is to the employee

In summary, measuring other variables such as employee engagement, job performance, the personality of the employee, and psychological wellbeing could be very useful to understanding the full picture of employee job satisfaction (Wright & Cropanzano, 2000).

This is what makes employees happy at work – Michael C. Bush

Although the terms ‘job satisfaction’ and ‘employee engagement’ are used interchangeably, there are subtle differences (Abraham, 2012; Harter et al., 2002).

Employee engagement as a concept

• Job satisfaction refers to how satisfied employees are with their work or how much they enjoy their work. Satisfied employees have a positive attitude toward their work.
• Employee engagement can be defined as the employee’s involvement with their work and includes their satisfaction and enthusiasm for their work. Employees who are engaged have a good work relationship with their colleagues, are interested in the company’s aim and products, are dedicated to their job, and will put in more time because they are committed to the work.

Although the concepts differ in definition, they are still related. Employee engagement is influenced by job satisfaction; employees with higher job satisfaction are more engaged (Garg & Kumar, 2012). Job satisfaction, however, is only one component of employee engagement. Despite these nuanced differences, satisfaction tools might be called employee engagement tools.

Similarly, some job satisfaction research investigates ‘work engagement’ (Attridge, 2009). Work engagement is defined as the level of commitment, involvement, and enthusiasm for one’s work (Attridge, 2009). This definition overlaps with those for job satisfaction and employee engagement.

Measuring tools for employee engagement

The Utrecht Work Engagement Scale (UWES) is a 17-item tool that measures work placement engagement across three dimensions: vigor, dedication, and absorption (Schaufeli & Bakker, 2003). Each item is a statement (e.g., ‘At my work, I feel bursting with energy’), and the employee responds how frequently they experience each statement on a scale from 0 (Never) to 6 (Always/Every day).

To score the tool, an average response for each subscale and an overall average are calculated. This tool has been used extensively across different industries and has sound psychometric properties. The psychometric properties can be found in the test manual, which is available from Wilmar Schaufeli’s website , where the English version and other translations of the test can be found as well.

In a shortened version of the UWES, the 17-item tool was reduced to 9 items (Schaufeli, Bakker, & Salanova, 2006). The items that comprise the UWES-9 are labeled with asterisks in the UWES test manual referred to above.

Another tool that measures employee engagement is the Job Engagement Scale (JES; Rich, Lepine, & Crawford, 2010). This scale was developed to incorporate the job satisfaction theory of workplace engagement proposed by Kahn (1990). Rich et al. (2010) argue that some items of the UWES did not properly capture Kahn’s theory, and they developed a new scale to account for this.

More information about the validation process and psychometric properties of the tool can be found in Rich et al. (2010).

The JES comprises 18 items, which are answered on a scale from 1 ‘strongly disagree’ to 5 ‘strongly agree.’ The items in the JES measure engagement in three domains: physical, emotional, and cognitive. Domain scores are calculated by averaging responses across each domain, and an overall average is calculated by averaging across all items. Higher scores indicate higher engagement.

The full test can be found in Bruce Rich’s PhD thesis .

Regularly measuring work engagement can help identify workers who are at higher risk of burnout.

These measurements are more useful if there is a baseline for comparison. If you know what the employee’s baseline engagement score is before burnout is a possibility, then there is a useful comparison score for subsequent measurements.

Faragher et al. (2013) conducted a meta-analysis on the relationship between job satisfaction and health, and showed that:

• Higher job satisfaction was correlated with better physical health.
• Lower job satisfaction was strongly correlated with the presence of mental/psychosocial problems.
• Lower job satisfaction was strongly correlated with more experiences of burnout.
• Lower job satisfaction was moderately correlated with higher levels of depression, higher levels of anxiety, lower levels of self-esteem, and worse general mental health.

The main predictors of burnout and exhaustion are difficult job demands and a stressful working environment (Attridge, 2009). Here is a list of strategies that can be put into place to ease worker dissatisfaction (Grawitch, Gottschalk, & Munz, 2006; Warr, 2005):

• Remove or solve problems related to tasks, processes, and operations.
• Improve the ergonomics of the workplace.
• Add flexibility to workplace schedules.
• Promote and support work–life balance.
• Define tasks and roles more clearly.
• Allow employees to take part in decision making.
• Improve social relationships at work, and create opportunities for these relationships to be fostered.
• Praise, recognize, and reward hard work.
• Foster skill development.

We have several useful resources that can be used to increase job satisfaction.

W stands for Way Forward and the SMART + Goals Worksheet can be used to help with decision making, breaking down tasks into smaller subgoals, goal setting, and planning.

The Avoidance Plan Worksheet  can be used to help identify avoidant behaviors, which impede goal setting and planning.

The EQ 5-Point Tool , Anger Exit and Re-Entry Routines, and the Conflict Resolution Checklist are useful tools to assist with conflict resolution and difficult conversations. These three tools teach clients how to rely on empathetic techniques when having a potentially difficult conversation , as well as how to navigate these conversations without relying on reactionary emotions such as anger, frustration, and annoyance.

If you’re looking for more science-based ways to help others manage stress without spending hours on research and session prep, this collection contains 17 validated stress management tools for practitioners . Use them to help others identify signs of burnout and create more balance in their lives.

By measuring job satisfaction, employers are better prepared to make the changes that result in a healthier, happier work environment for their employees.

When measuring job satisfaction, remember the following:

• Don’t rely on only one measurement at one point in time. Try to measure job satisfaction over time so that you can track changes.
• Several variables can affect job satisfaction, including situational variables such as the work environment. Adjustments to these situational variables can improve job satisfaction for all employees.
• All measurements should be followed up with meetings and interviews so that you can better understand the employee’s situation.

A variety of job satisfaction tools are listed in this post. However, this list is not exhaustive. If you use a different tool in your workplace, then please share your experience and the name of the tool in the comments section. We love hearing from you and learning more about your work.

We hope you enjoyed reading this article. Don’t forget to download our three Work & Career Coaching Exercises for free .

• Abraham, S. (2012). Job satisfaction as an antecedent to employee engagement. SIES Journal of Management , 8(2), 27–36.
• Andrews, F. M., & Withey, S. B. (2012). Social indicators of well-being: Americans’ perceptions of life quality . Springer Science & Business Media.
• Attridge, M. (2009). Measuring and managing employee work engagement: A review of the research and business literature. Journal of Workplace Behavioral Health , 24 (4), 383–398.
• Earl, J. K., Minbashian, A., Sukijjakhamin, A., & Bright, J. E. (2011). Career decision status as a predictor of resignation behavior five years later. Journal of Vocational Behavior , 78 (2), 248–252.
• Faragher, E. B., Cass, M., & Cooper, C. L. (2013). The relationship between job satisfaction and health: A meta-analysis. In C. L. Cooper (Ed.) From stress to wellbeing (vol. 1) (pp. 254–271). Palgrave Macmillan.
• Gallup Organization. (1992–1999). Gallup Workplace Audit (Copyright Registration Certificate TX-5 080 066). U.S. Copyright Office
• Garg, A., & Kumar, V. (2012). A study of employee engagement in pharmaceutical sector. International Journal of Research in IT and Management , 2 (5), 85–98.
• Grawitch, M. J., Gottschalk, M., & Munz, D. C. (2006). The path to a healthy workplace: A critical review linking healthy workplace practices, employee well-being, and organizational improvements. Consulting Psychology Journal: Practice and Research , 58 (3), 129–147.
• Hackman, J. R., & Oldham, G. R. (1974). The Job Diagnostic Survey: An instrument for the diagnosis of jobs and the evaluation of job redesign projects. JSAS Catalog of Selected Documents in Psychology , 4 , 148.
• Hackman, J. R., & Oldham, G. R. (1975). Development of the job diagnostic survey. Journal of Applied Psychology , 60 (2), 159–170.
• Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology , 87 (2), 268–279.
• Harter, J. K., Schmidt, F. L., Killham, E. A., & Agrawal, S. (2009). Q12 meta-analysis: The relationship between engagement at work and organizational outcomes . Gallup.
• Ironson, G. H., Smith, P. C., Brannick, M. T., Gibson, W. M., & Paul, K. B. (1989). Construction of a Job in General scale: A comparison of global, composite, and specific measures. Journal of Applied Psychology , 74 (2), 193–200.
• Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001). The job satisfaction–job performance relationship: A qualitative and quantitative review. Psychological Bulletin , 127 (3), 376–407.
• Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal , 33 , 692–724.
• Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and effects on job performance. Academy of Management Journal , 53 (3), 617–635.
• Schaufeli, W. B., & Bakker, A. B. (2003). Test manual for the Utrecht Work Engagement Scale . Unpublished manuscript, Utrecht University, The Netherlands. Retrieved from http://www.schaufeli.com
• Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement , 66 (4), 701–716.
• Schaufeli, W. B., Taris, T. W., & Van Rhenen, W. (2008). Workaholism, burnout, and work engagement: Three of a kind or three different kinds of employee well‐being? Applied Psychology , 57 (2), 173–203.
• Smith, P. C., Kendall, L. M., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement: A strategy for the study of attitudes . Rand McNally.
• Spector, P. E. (1985). Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey. American Journal of Community Psychology , 13 (6), 693–713.
• van Saane, N., Sluiter, J. K., Verbeek, J. H. A. M., & Frings-Dresen, M. H. W. (2003). Reliability and validity of instruments measuring job satisfaction: A systematic review. Occupational Medicine , 53 (3), 191–200.
• Warr, P. (2005). Work, well-being, and mental health. In J. Barling, E. K. Kelloway, & M. R. Frone (Eds.), The handbook of work stress (pp. 547–573). Sage
• Wright, T. A., & Cropanzano, R. (2000). Psychological well-being and job satisfaction as predictors of job performance. Journal of Occupational Health Psychology , 5 (1), 84–94.

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Please suggest me some tools for measuring Impact of HRM practices and job satisfaction level for employees in pharmaceutical company

Could you please specify the types of tools you’re looking for? I am happy to assist you to the best of my abilities!

Warm regards, Julia | Community Manager

Hi Nicole, I would appreciate if you could suggest me a measurement tool to measure job satisfaction and performance among nurses. Thank you

I found two scales that seem to measure what you’re looking for: the Nursing Job Satisfaction Scale (NJSS) and the Nurses’ Professional Commitment Scale (NPCS). You can find more information on the two tools here .

Hope this information helps you! Kind regards, Julia | Community Manager

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Psychometric properties and criterion related validity of the Norwegian version of hospital survey on patient safety culture 2.0

• Espen Olsen 1 ,
• Seth Ayisi Junior Addo 1 ,
• Susanne Sørensen Hernes 2 , 3 ,
• Marit Halonen Christiansen 4 ,
• Arvid Steinar Haugen 5 , 6 &
• Ann-Chatrin Linqvist Leonardsen 7 , 8  

BMC Health Services Research volume  24 , Article number:  642 ( 2024 ) Cite this article

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Several studies have been conducted with the 1.0 version of the Hospital Survey on Patient Safety Culture (HSOPSC) in Norway and globally. The 2.0 version has not been translated and tested in Norwegian hospital settings. This study aims to 1) assess the psychometrics of the Norwegian version (N-HSOPSC 2.0), and 2) assess the criterion validity of the N-HSOPSC 2.0, adding two more outcomes, namely ‘pleasure of work’ and ‘turnover intention’.

The HSOPSC 2.0 was translated using a sequential translation process. A convenience sample was used, inviting hospital staff from two hospitals ( N  = 1002) to participate in a cross-sectional questionnaire study. Data were analyzed using Mplus. The construct validity was tested with confirmatory factor analysis (CFA). Convergent validity was tested using Average Variance Explained (AVE), and internal consistency was tested with composite reliability (CR) and Cronbach’s alpha. Criterion related validity was tested with multiple linear regression.

The overall statistical results using the N-HSOPSC 2.0 indicate that the model fit based on CFA was acceptable. Five of the N-HSOPSC 2.0 dimensions had AVE scores below the 0.5 criterium. The CR criterium was meet on all dimensions except Teamwork (0.61). However, Teamwork was one of the most important and significant predictors of the outcomes. Regression models explained most variance related to patient safety rating (adjusted R 2  = 0.38), followed by ‘turnover intention’ (adjusted R 2  = 0.22), ‘pleasure at work’ (adjusted R 2  = 0.14), and lastly, ‘number of reported events’ (adjusted R 2= 0.06).

The N-HSOPSC 2.0 had acceptable construct validity and internal consistency when translated to Norwegian and tested among Norwegian staff in two hospitals. Hence, the instrument is appropriate for use in Norwegian hospital settings. The ten dimensions predicted most variance related to ‘overall patient safety’, and less related to ‘number of reported events’. In addition, the safety culture dimensions predicted ‘pleasure at work’ and ‘turnover intention’, which is not part of the original instrument.

Peer Review reports

Patient harm due to unsafe care is a large and persistent global public health challenge and one of the leading causes of death and disability worldwide [ 1 ]. Improving safety in healthcare is central in governmental policies, though progress in delivering this has been modest [ 2 ]. Patient safety culture surveys have been the most frequently used approach to measure and monitor perception of safety culture [ 3 ]. Safety culture is defined as “the product of individual and group values, attitudes, perceptions, competencies and patterns of behavior that determine the commitment to, and the style and proficiency of, an organization’s health and safety management” [ 4 ]. Moreover, safety culture refers to the perceptions, beliefs, values, attitudes, and competencies within an organization pertaining to safety and prevention of harm [ 5 ]. The importance of measuring patient safety culture was underlined by the results in a 2023 scoping review, where 76 percent of the included studies observed associations between improved safety culture and reduction of adverse events [ 6 ].

To assess patient safety culture in hospitals the US Agency for Healthcare Research and Quality (AHRQ) launched the Hospital Survey on Patient Safety Culture (HSOPSC) version 1.0 in 2004 [ 7 , 8 ]. Since then, HSOPSC 1.0 has become one of the most used tools to evaluate patient safety culture in hospitals, administered to approximately hundred countries and translated into 43 languages as of September 2022 [ 9 ]. HSOPSC 1.0 has generally been considered to be one of the most robust instrument measuring patient safety culture, and it has adequate psychometric properties [ 10 ]. In Norway, the first studies using N-HSOPSC 1.0 concluded that the psychometric properties of the instrument were satisfactory for use in Norwegian hospital settings [ 11 , 12 , 13 ]. A recent review of literature revealed 20 research articles using the N-HSOPSC 1.0 [ 14 ].

Studies of safety culture perceptions in hospitals require valid and psychometric sound instruments [ 12 , 13 , 15 ]. First, an accurate questionnaire structure should demonstrate a match between the theorized content structure and the actual content structure [ 16 , 17 ]. Second, psychometric properties of instruments developed in one context is required to demonstrate appropriateness in other cultures and settings [ 16 , 17 ]. Further, psychometric concepts need to demonstrate relationships with other related and valid criteria. For example, data on criterion validity can be compared with criteria data collected at the same time (concurrent validity) or with similar data from a later time point (predictive validity) [ 12 , 16 , 17 ]. Finally, researchers need to demonstrate a match between the content theorized to be related to the actual content in empirical data [ 15 ]. If these psychometric areas are not taken seriously, this may lead to many pitfalls both for researchers and practitioners [ 14 ]. Pitfalls might be imprecise diagnostics of the patient safety level and failure to evaluate effect of improvement initiative. Moreover, researchers can easily erroneously confirm or reject research hypothesis when applying invalid and inaccurate measurement tools.

Patient safety cannot be understood as an isolated phenomenon, but is influenced by general job characteristics and the well-being of the individual health care workers. Karsh et al. [ 18 ] found that positive staff perceptions of their work environment and low work pressure were significantly related to greater job satisfaction and work commitment. A direct association has also been reported between turnover and work strain, burnout and stress [ 19 ] Zarei et al. [ 20 ] showed a significant relationship between patient safety (safety climate) and unit type, job satisfaction, job interest, and stress in hospitals. This study also illustrated a strong relationship between lack of personal accomplishment, job satisfaction, job interest and stress. Also, there was a negative correlation between occupational burnout and safety climate, where a decrease in the latter was associated with an increase in the former. Hence, patient safety researchers should look at healthcare job characteristics in combination with patient safety culture.

Recently, the AHRQ revised the HSOPSC 1.0 to a 2.0 version, to improve the quality and relevance of the instrument. HSOPSC 2.0 is shorter, with 25 items removed or with changes made for response options and ten additional items added. HSOPSC 2.0 was validated during the revision process [ 21 ], but the psychometric qualities across cultures, countries and in different settings need further investigation. Consequently, the overall aim of this study was to investigate the psychometric properties of the HSOPSC 2.0 [ 21 ] (see supplement 1) in a Norwegian hospital setting. Specifically, the aims were to 1) assess the psychometrics of the Norwegian version (N-HSOPSC 2.0), and 2) assess the criterion validity of the N-HSOPSC 2.0, adding two more outcomes, namely’ pleasure of work’ and ‘turnover intention’.

This study had cross‐sectional design, using a web-based survey solution called “Nettskjema” to distribute questionnaires in two Norwegian hospitals. The study adheres to The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)Statement guidelines for reporting observational studies [ 22 ].

Translation of the HSOPSC 2.0

We conducted a «forward and backward» translation in-line with recommendations from Brislin [ 23 ]. First, the questionnaires were translated from English to Norwegian by a bilingual researcher. The Norwegian version was then translated back to English by another bilingual researcher. Thereafter, the semantic, idiopathic and conceptual equivalence between the two versions were compared by the research group, consisting of experienced researchers. The face value of the N-HSOPSC 2.0-version was considered to be adequate and the items lend themselves well to the corresponding latent concepts.

The N-HSOPSC 2.0 was pilot-tested with focus on content and face validity. Six randomly selected healthcare personnel were asked to assess whether the questionnaire was adequate, appropriate, and understandable regarding language, instructions, and scores. In addition, an expert group consisting of senior researchers ( n  = 4) and healthcare personnel ( n  = 6), with competence in patient safety culture was asked to assess the same.

The questionnaire

The HSOSPS 2.0 (supplement 1) consists of 32 items using 5-point Likert-like scales of agreement (from 1 = strongly disagree to 5 = strongly agree) or frequency (from 1 = never to 5 = always), as well as an option for “does not apply/do now know”. The 32 items are distributed over ten dimensions. Additionally, 2-single item patient safety culture outcome measures, and 6-item background information measures are included. The patient safety culture single item outcome measures evaluate the overall ‘patient safety rating’ for the work area, and ‘reporting patient safety events’.

In addition to the N-HSOPSC 2.0, participants were asked to respond to three questions about their ‘pleasure at work’ (measure if staff enjoy their work, and are pleased with their work, scored from 1 = never, to 4 = always) [ 24 ], two questions about their ‘intention to quit’ (measure is staff are considering to quit their job, scored on a 5-point likert scale where 1 = strongly agree to 5 = strongly disagree) [ 25 ], as well as demographic variables (gender, age, professional background, primary work area, years of work experience).

Participants and procedure

The data collection was conducted in two phases: the first phase (Nov-Dec 2021) at Hospital A and the second phase at Hospital B (Feb-March 2022)). We used a purposive sampling strategy: At Hospital A (two locations), all employees were invited to participate ( N  = 6648). This included clinical staff, administrators, managers, and technical staff. At Hospital B (three locations) all employees from the anesthesiology, intensive care and operation wards were invited to participate ( N  = 655).

The questionnaire was distributed by e-mail, including a link to a digital survey solution delivered by the University of Oslo, and gathered and stored on a safe research platform: TSD (services for sensitive data). This is a service with two-factor authentication, allowing data-sharing between the collaborating institutions without having to transfer data between them. The system allows for storage of indirectly identifying data, such as gender, age, profession and years of experience, as well as hospital. Reminders were sent out twice.

Statistical analyses

Data were analyzed using Mplus. Normality was assessed for each item using skewness and kurtosis, where values between + 2 and -2 are deemed acceptable for normal distribution [ 26 ]. Missing value analysis was conducted using frequencies, to check the percentage of missing responses for each item. Correlations were assessed using Spearman’s correlation analysis, reported as Cronbach’s alpha.

Confirmatory factor analysis (CFA) was conducted to test the ten-dimension structure of the N-HSOPSC 2.0 using Mplus and Mplus Microsoft Excel Macros. The structure was then tested for fitness using Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) [ 27 ]. Table 1 shows the fitness indices and acceptable thresholds.

Reliability of the 10 predicting dimensions were also assessed using composite reliability (CR) values, where 0.7 or above is deemed acceptable for ascertaining internal consistency [ 25 ].

Convergent validity was assessed using the Average Variance Explained (AVE), where a value of at least 0.5 is deemed acceptable [ 28 ], indicating that at least 50 percent of the variance is explained by the items in a dimension. Criterion-related validity was tested using linear regression, adding ‘turnover intention’ and ‘pleasure at work’ to the two single item outcomes of the N-HSOPSC 2.0.

Internal consistency and reliability were assessed using Cronbach’s alpha, where values > 0.9 is assumed excellent, > 0.8 = good, > 0.7 = acceptable, > 0.6 = questionable, > 0.5 = poor and < 0.5 = unacceptable [ 29 ].

Ethical considerations

The study was conducted in-line with principles for ethical research in the Declaration of Helsinki, and informed consent was obtained from all the participants [ 30 ]. Completed and submitted questionnaires were assumed as consent to participate. Data privacy protection was reviewed by the respective hospitals’ data privacy authority, and assessed by the Norwegian Center for Research Data (NSD, project number 322965).

In total, 1002 participants responded to the questionnaire, representing a response rate of 12.6 percent. As seen in Table  2 , 83.7% of the respondents worked in Hospital A and the remaining 16.3% in Hospital B. The majority of respondents (75.7%) were female, and 75.9 percent of respondents worked directly with patients.

The skewness and kurtosis were between + 2 and -2, indicating that the data were normally distributed. All items had less than two percent of missing values, hence no methods for calculating missing values were used.

Correlations

Correlations and Cronbach’s alpha are displayed in Table  3 .

The following dimensions had the highest correlations; ‘teamwork’, ‘staffing and work pace’, ‘organizational learning-continuous improvement’, ‘response to error’, ‘supervisor support for patient safety’, ‘communication about error’ and ‘communication openness’. Only one dimension, ‘teamwork’ (0.58), had a Cronbach’s alpha below 0.7 (acceptable). Hence, most of the dimensions indicated adequate reliability. Higher levels of the 10 safety dimensions correlate positively with patient safety ratings.

Confirmatory Factor Analysis (CFA)

Table 4 shows the results from the CFA. CFA ( N  = 1002) showed acceptable fitness values [CFI = 0.92, TLI = 0.90, RMSEA = 0.045, SRMR = 0.053] and factor loadings ranged from 0.51–0.89 (see Table  1 ). CR was above the 0.70 criterium on all dimensions except on ‘teamwork’ (0.61). AVE was above the 0.50 criterium except on ‘teamwork’ (0.35), ‘staffing and work pace’ (0.44), ‘organizational learning-continuous improvement’ (0.47), ‘response to error’ (0.47), and communication openness.

Criterion validity

Independent dimensions of HSOPSC 2.0 were employed to predict four different criteria: 1) ‘number of reported events’, 2) ‘patient safety rating’, 3) ‘pleasure at work’, and 4) ‘turnover intentions’. The composite measures explained variance of all the outcome variables significantly thereby ascertaining criterion-related validity (Table  5 ). Regression models explained most variance related to ‘patient safety rating’ (adjusted R 2  = 0.38), followed by ‘turnover intention’ (adjusted R 2  = 0.22), ‘pleasure at work’ (adjusted R 2  = 0.14), and lastly, number of reported events (adjusted R 2  = 0.06).

In this study we have investigated the psychometric properties of the N-HSOPSC 2.0. We found the face and content validity of the questionnaire satisfactory. Moreover, the overall statistical results indicate that the model fit based on CFA was acceptable. Five of the N-HSOPSC 2.0 dimensions had AVE scores below the 0.5 criterium, but we consider this to be the strictest criterium employed in the evaluations of the psychometric properties. The CR criterium was met on all dimensions except ‘teamwork’ (0.61). However, ‘teamwork’ was one of the most important and significant predictors of the outcomes. One the positive side, the CFA results supports the dimensional structure of N-HSOPSC 2.0, and the regression results indicate a satisfactory explanation of the outcomes. On the more critical side, particularly AVE scores reflect threshold below 0.5 on five dimensions, indicating items have certain levels of measurement error as well.

In our study, regression models explained most variance related to ‘patient safety rating’ (R 2  = 0.38), followed by ‘turnover intention’ (R 2  = 0. 22), ‘pleasure at work’ (R 2  = 0.14), and lastly, number of reported events (R 2  = 0.06). This supports the criterion validity of the independent dimensions of N-HSOSPC 2.0, also when adding ‘turnover intention’ and ‘pleasure at work’. These results confirm previous research on the original N-HSOPSC 1.0 [ 12 , 13 ]. The current study also found that ‘number of reported events’ was negatively related to safety culture dimensions, which is also similar to the N-HSOPSC 1.0 findings [ 12 , 13 ].

The current study did more psychometric assessments compared to the first Norwegian studies using HSOPSC 1.0 [ 11 , 12 , 13 ]. However, results from the current study still support that the overall reliability and validity of N-HSOPSC 2.0 when comparing the results with the first studies using N-HSOPSC 1.0 [ 11 , 12 , 13 ]. Also, based on theory and expectations, the dimensions predicted ‘pleasure at work’ and ‘overall safety rating’ positively, and ‘turnover intentions’ and ‘number of reported events’ negatively. The directions of the relations thereby support the overall criterion validity. Some of the dimensions do not predict the outcome variables significantly, nonetheless, each criterion related significantly to at least two dimensions on the HSOPSC 2.0. It is also worth noticing that ‘teamwork’ was generally one of the most important predictors even thought this dimension had the lowest convergent validity (AVE) in the previous findings [ 11 , 12 , 13 ], even if the strict AVE criterium was not satisfactory on the teamwork dimension and CR was also below 0.7. Since the explanatory power of teamwork was satisfactory, this illustrate that the AVE and CR criteria are maybe too strict.

The sample in the current study consisted of 1009 employees at two different hospital trusts in Norway and across different professions. The gender and ages are representative for Norwegian health care workers. In total 760 workers had direct patient contact, 167 had not, and 74 had patient contact sometimes. We think this mix is interesting, since a system perspective is key to establishing patient safety [ 31 ]. The other background variables (work experience, age, primary work area, and gender) indicate a satisfactory spread and mix of personnel in the sample, which is an advantage since then the sample to a large extend represent typical healthcare settings in Norway.

In the current study, N-HSOPSC 2.0 had higher levels of Cronbach’s alpha than in the first N-HSOPSC 1.0 studies [ 11 , 13 ], but more in-line with results from a longitudinal Norwegian study using the N-HSOPSC 1.0 in 2009, 2010 and 2017 respectively [ 23 ]. Moreover, the estimates in the current study reveal a higher level of factor loading on the N-HSOPSC 2.0, ranging from 0.51 to 0.89. This is positive since CFA is a key method when assessing the construct validity [ 16 , 17 , 32 ].

AVE and CR were not estimated in the first Norwegian HSOPSC 1.0 studies [ 11 , 13 ]. The results in this study indicate some issues regarding particularly AVE (convergent validity) since five of the concepts were below the recommended 0.50 threshold [ 32 ]. It is also worth noticing that all measures in the N-HSOPSC 2.0, except ‘teamwork’ (CR = 61), had CR values above 0.70, which is satisfactory. AVE is considered a strict and more conservative measure than CR. The validity of a construct may be adequate even though more than 50% of the variance is due to error [ 33 ]. Hence, some AVE values below 0.50 is not considered critical since the overall results are generally satisfactory.

The first estimate of the criterion related validity of the N-HSOPSC 2.0 using multiple regression indicated that two dimensions where significantly related to ‘number of reported events’, while six dimensions were significantly related to ‘patient safety rating’. The coefficients were negatively related with number of reported events, and positively related with patient safety rating, as expected. In the first Norwegian study in Norway on the N-HSOPSC 1.0 [ 13 ], five dimensions were significantly related to ‘number of reported events’, and seven dimensions were significantly related to ‘patient safety ratings’. The relations with ‘numbers of events reported’ were then both positive and negative, which is not optimal when assessing criterion validity. Hence, since all significant estimates are in the expected directions, the criterion validity of N-HSOPSC 2.0 has generally improved compared to the previous version.

In the current study we added ‘pleasure at work’ and ‘turnover intention’ to extend the assessment of criterion related validity. The first assessment indicated that ‘teamwork’ had a very substantial and positive influence on ‘pleasure at work’. Moreover, ‘staffing and work pace’ also had a positive influence on ‘pleasure at work’, but none of the other concepts were significant predictors. Hence, the teamwork dimension is key in driving ‘pleasure at work’, then followed by ‘staffing and working pace’. ‘Turnover intentions’ was significantly and negatively related to ‘teamwork’, ‘staffing and working pace’, ‘response to error’ and ‘hospital management support’. Hence, the results indicate these dimensions are key drivers in avoiding turnover intentions among staff in hospitals. A direct association has been reported between turnover and work strain, burnout and stress [ 19 ]. Zarei et al. [ 20 ] showed a significant relationship between patient safety (safety climate) and unit type, job satisfaction, job interest, and stress in hospitals. This study also illustrated a strong relationship between lack of personal accomplishment, job satisfaction, job interest and stress. Furthermore, a negative correlation between occupational burnout and safety climate was discovered, where a decrease in the latter is associated with an increase in the former [ 20 ]. Hence, patient safety researchers should look at health care job characteristics in combination with patient safety culture.

Assessment of psychometrics must consider other issues beyond statistical assessments such as theoretical consideration and face validity [ 16 , 17 ]; we believe one of the strengths of the HSOPSC 1.0 is that the instrument was operationalized based on theoretical concepts. This has been a strength, as opposed to other instruments built on EFA and a random selection of items included in the development process. We believe this is also the case in relation to HSOPSC 2.0; the instrument is theoretically based, easy to understand, and most importantly, can function as a tool to improve patient safety in hospitals. Moreover, when assessing the items that belongs to the different latent constructs, item-dimension relationships indicate a high face validity.

Forthcoming studies should consider predicting other outcomes, such as for instance mortality, morbidity, length of stay and readmissions, with the use of N-HSOPSC 2.0.

Limitations

This study is conducted in two Norwegian public hospital trusts, indicating some limitations about generalizability. The response rate within hospitals was low and therefore we could not benchmark subgroups. However, this was not part of the study objectives. The response rate may be hampered by the pandemic workload, and high workload in the hospitals. However, based on the diversity of the sample, we find the study results robust and adequate to explore the psychometric properties of N-HSOPSC 2.0. For the current study, we did not perform sample size calculations. With over 1000 respondents, we consider the sample size adequate to assess psychometric properties. Moreover, the low level of missing responses indicate N-HSOPSC 2.0 was relevant for the staff included in the study.

There are many alternative ways of exploring psychometric capabilities of instruments. For example, we did not investigate alternative factorial structures, e.g. including hierarchical factorial models or try to reduce the factorial structure which has been done with N-HSOPSC 1.0 short [ 34 ]. Lastly, we did not try to predict patient safety indicators over time using a longitudinal design and other objective patient safety indicators.

The results from this study generally support the validity and reliability of the N-HSOPSC 2.0. Hence, we recommend that the N-HSOPSC 2.0 can be applied without any further adjustments. However, future studies should potentially develop structural models to strengthen the knowledge and relationship between the factors included in the N-HSOPSC 2.0/ HSOPSC 2.0. Both improvement initiatives and future research projects can consider including the ‘pleasure at work’ and ‘turnover intentions’ indicators, since N-HSOPSC 2.0 explain a substantial level of variance relating to these criteria. This result also indicates an overlap between general pleasure at work and patient safety culture which is important when trying to improve patient safety.

Availability of data and materials

Datasets generated and/or analyzed during the current study are not publicly available due to local ownership of data, but aggregated data are available from the corresponding author on reasonable request.

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Acknowledgements

Master student Linda Eikemo is acknowledged for participating in the data collection in Hospital A, and Nina Føreland in Hospital B.

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Department of Anaesthesia and Intensive Care, Haukeland University Hospital, Bergen, Norway

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EO, ASH and ACLL initiated the study. All authors (EO, SA, SSH, MHC, ASH, ACLL) participated in the translation process. SSH and ACLL were responsible for data collection. EO and SA performed the statistical analysis, which was reviewed by ASH and ACLL. EO, SA and ACLL wrote the initial draft of the manuscript, and all authors (EO, SA, SSH, MHC, ASH, ACLL) critically reviewed the manuscript. All authors(EO, SA, SSH, MHC, ASH, ACLL) have read and approved the final version of the manuscript.

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Correspondence to Ann-Chatrin Linqvist Leonardsen .

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The study was conducted in-line with principles for ethical research in the Declaration of Helsinki, and informed consent was obtained from all the participants [ 30 ]. Eligible healthcare personnel were informed of the study through hospital e-mails and by text messages. Completed and submitted questionnaires were assumed as consent to participate. According to the Norwegian Health Research Act §4, no ethics approval is needed when including healthcare personnel in research.

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Olsen, E., Addo, S.A.J., Hernes, S.S. et al. Psychometric properties and criterion related validity of the Norwegian version of hospital survey on patient safety culture 2.0. BMC Health Serv Res 24 , 642 (2024). https://doi.org/10.1186/s12913-024-11097-7

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Want a career in the U.S. government? These are the best and worst agencies to work at, according to a survey of over 1 million federal employees

Exploring the cosmos makes for happy employees, federal workers like to  work from home  like everyone else, and an agency that has struggled with low morale is showing improvement.

Those are some of the highlights of  a survey released Monday  of more than a million federal workers.

In a city that revolves around the federal government, the annual Best Places to Work survey is a closely watched annual event worthy of bragging rights — provided you’re one of the agencies such as NASA or the Government Accountability Office who topped the survey.

The survey uses information from the Office of Personnel Management’s Federal Employee Viewpoint Survey and is produced by the Partnership for Public Service and the Boston Consulting Group .

It covers 532 federal agencies including 17 large agencies, 26 midsize agencies, 30 small agencies and 459 subcomponents. The rankings first came out in 2003, and agencies that do well are known to post the results on their websites.

NASA has held the top spot for 12 years, a fact that the agency touted on its website as the agency’s administrator,  Bill Nelson, praised the staff  as a “team of wizards.”

NASA topped the list of large agencies, while the Government Accountability Office — often called the “congressional watchdog” because they examine how government money is spent — topped the list of mid-size agencies. The National Indian Gaming Commission, making its first appearance on the survey, was first among small agencies.

On the opposite end of the scale, the Social Security Administration remained in last place among the 17 large agencies. Scores for the Department of State and the U.S. Agency for International Development declined for the second year in a row putting them near the bottom in their respective categories. The Export-Import Bank of the United States was at the bottom of the small agencies category  while the Federal Bureau of Prisons , with a score of 38.1 out 100, was at the bottom of the subcomponents list.

The survey measures job satisfaction and engagement on a scale of zero to 100. The survey found that overall job satisfaction and engagement across the federal workforce ticked up a bit to 65.7; that’s a 2.3-point increase over 2022’s figures.

Among large agencies, the Department of Homeland Security saw the most improvement. The department is the third largest in the federal government with roughly 260,000 employees who do everything from respond to natural disasters to patrol the border with Mexico. Created in the wake of the Sept. 11, 2001 terrorist attack, it’s often struggled with morale problems.

The survey didn’t specify what the agency had done to climb in the rankings but one answer could be found on the Reddit subgroup for federal employees. The  agency’s leader, Secretary Alejandro Mayorkas,  may go down in history as only the second Cabinet member to be impeached over Republican anger at his role in immigration but among the agency’s vast workforce he’s sometimes referred to as St. Mayorkas for his liberal time-off policies. Under his watch, the frontline workforce at the Transportation Security Administration has also received significant pay raises.

The secretary praised the workforce in a statement Monday and said the department has been focused on engaging with its workforce to figure out what their needs are.

“The impact of this line of effort is real. I am very proud that, for the second consecutive year, our Department has shown significant improvement in its employee well-being and satisfaction,” the secretary said.

In a key post-pandemic development, telework is proving to be as popular with federal government workers as it is among the private sector. Federal employees who teleworked fulltime registered the highest scores — 74.6 out of 100 — compared to others who worked at headquarters or field offices. The data shows that nearly 54% of federal employees have a hybrid work schedule while 14% telework fulltime. About 32% go into their job site daily. Those figures are largely the same as in 2022.

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How-To Geek

How to improve your linkedin profile using free ai tools.

In the post-ChatGPT job market, you need to fight AI with AI.

Quick Links

[optional] step 0: analyze your linkedin profile using ai, step 1: make your profile pic more professional, step 2: write a compelling "headline" and "about" section, step 3: write descriptions for "work experience" and "education", step 4: design a custom profile background image.

Do you, like me, feel that improving your LinkedIn profile is a boring and tedious chore? Well, these new AI tools certainly make the task more manageable and even a little fun. So join me as I use free AI tools to improve my LinkedIn.

Keeping in line with the overall AI theme of this article, I wanted to have my LinkedIn profile analyzed and rated by an AI tool and see what it had to say. I checked multiple tools and found Taplio's LinkedIn Profile Optimization tool the most comprehensive, with feedback that adheres to the best practices.

The tool is free to use, and you can certainly check it out to get some pointers. However, this is not a necessary step. In fact, a more useful step would be to manually check a few prolific LinkedIn accounts in your field and see how they have set up their profiles. You can take inspiration from those accounts and optimize your LinkedIn accordingly. Alternatively, you can consult your friends and colleagues and get their feedback on your profile.

As a rule of thumb, your LinkedIn profile is solid if it checks the following boxes:

• A professional-looking profile picture.
• A compelling Headline that helps you standout.
• An engaging About section (formerly Summary) to offer more insight into your professional background.
• A detailed look at your work experience and educational background.
• A custom cover photo that encapsulates who you are as a person and what it's like working with you.
• Routinely writing LinkedIn posts and engaging with other people's content.

So, let's see how we can use AI to help us with these tasks.

LinkedIn is a professional social media network, so you need to upload a professional-looking profile picture to get the best results, i.e., a response from potential employers. Here are the best practices for nailing your LinkedIn profile pic:

• Wear professional attire.
• Use an uncluttered, preferably white background.
• Have a relaxed, professional pose.
• Boast a natural and friendly expression.
• Ensure your face is well-lit.

For reference, this was my profile picture at the time of writing:

While I wouldn't call it the worst , the fact that a person is sitting in the background does make it feel a bit casual and unprofessional. Unfortunately, I really don't have any selfies in my gallery that meet all the mentioned criteria. And since I'm just too lazy to suit up, go outside, and click a good picture, let's just use AI to fix this issue.

First, I'll head over to this website: remove.bg . It's an AI tool where you can upload your picture, and it'll remove the background from it. The tool also allows you to Add a Background like scenery or just a solid color. I used it to add a white background to the image.

Once done, hit the Download button to download the image to your local storage.

The image we got is 293 x 293 px. This is not a fault of the background removal process. The original image I was using was heavily cropped-in and low res.

Next, I'll head over to this website: upscale.media . This is an optional step that is only recommended if you're dealing with a low res image—like mine—that you wish to upscale. Simply click Upload Image and select the image that you want to upscale. You'll get 2 images, one with normal upscaling and the other with AI upscaling (with the option to Upscale to 2x or 4x size and "Enhance Quality"). Pick the one you like (I picked the AI Upscaled Image) and click "Download Image."

Now, just upload this image to your LinkedIn profile pic, and you're one step closer to looking more professional.

It is still recommended that you use a professional headshot and post it on LinkedIn, especially before you start job hunting and networking. But in the meantime, an AI-optimized image can be a decent placeholder.

The Headline is the brief line of text (extendable up to two lines) that appears directly below your name. Whereas the About section is a dedicated text block where you can talk more about yourself and give a broad overview of your professional life.

Both sections are there to help you sell yourself to potential clients. But as you can see, my lackluster self-description isn't doing me any favors. So, let's use AI to fix this.

I tried this free LinkedIn Headline Generator from Taplio but found the output could have been better. I imagine the problem was with the limited data I gave it to work with. But then again, I truly couldn't figure out what extra info to add. So I decided to use ChatGPT (the free version running GPT3.5 will do) and entered this prompt:

Imagine you are an experienced LinkedIn profile strategist. Following the industry's best practices, create a LinkedIn headline to help me stand out and make me interesting so other LinkedIn users might want to connect with me. First, point out what are the best practices for crafting a LinkedIn Headline. Then, ask me a series of questions, so you know more about me so you can craft the headline according to the best practices. Ensure your questions are insightful and your headline suggestions are precise and compelling.

As instructed in the prompt, ChatGPT proceeded to ask me a few questions and, based on my responses, came up with this Headline:

Tech Writer & Storyteller | How-To Geek Contributor | Authority Hacker Freelancer | Data-Driven Narratives for Tech & SaaS

This is way better than the previous headline! Of course, you are free to edit this and also prompt ChatGPT to generate a few more alternative headlines if you don't like the first one.

Coming to the About section, Taplio's LinkedIn Summary Generator was pretty good. I particularly found it does a better job with a Humorous tone.

The output could have benefited from a little more context, so I plugged this into ChatGPT again and added the following prompt:

You're an expert in crafting LinkedIn profile summaries. Your task is to critique and enhance the LinkedIn About section provided by the user. The user will share their current profile summary with you. Start by analyzing the strengths and weaknesses of the existing summary, focusing on clarity, relevance, and tone. Then, engage the user with specific questions to gather additional information that can be incorporated into the summary. Ensure the questions are structured to guide the user in providing detailed and relevant responses without requiring extensive thought. Finally, use the gathered information to optimize the LinkedIn About section, maintaining the user's style and tone. Your goal is to create a compelling and professional summary that effectively showcases the user's skills, experiences, and aspirations.

This is my LinkedIn About section or Summary section:

[Insert Taplio provided summary here]

And with that, this is my new AI-generated LinkedIn About section:

All that remains now is to plug both the Headline and About sections into LinkedIn.

For the Work Experience and Education sections, you should provide as much detail as possible so that people who land on your LinkedIn profile can get a clear picture of your professional and academic background. There isn't a particular AI tool that can help you at this stage. You basically need to sit and journal about all the places you worked at and all the different certificates you hold.

Here's a really helpful article on Writing Your LinkedIn Experience Section . It contains a lot of actionable tips and tricks that apply to both the Work Experience and Education sections. Now, while populating the sections with relevant data, you will come across a field called "Description," where you get to go into more detail about your experience in that position to woo any profile visitors. Now, this is an area where AI can come in handy. Just paste the following Prompt into ChatGPT:

You are a meticulous detective, and I am a reluctant witness. Your task is to help me write a comprehensive and engaging LinkedIn profile description for one specific work experience or educational qualification at a time. Start by asking if I want to discuss my work experience or education. Based on my response, ask relevant, detailed questions designed to uncover all necessary information, even if I'm reluctant to provide it. And try to point out specific information if I am providing vague answers. Try to ask for numbers or encourage me to provide numbers.

**Begin the questioning:**

"Let's start with the basics. Do you want to discuss your work experience or your education?"

**If Work Experience:**

"Alright, tell me the name of the company and your job title there. For example, 'Google, Software Engineer'."

"What were the dates of your employment at [Company Name]? Be specific. For instance, 'June 2015 - Present'."

- (If still employed, note that the end date is 'present'.)

"What are/were your main responsibilities at [Company Name]? Describe your day-to-day tasks. For example, 'Developed and maintained web applications using JavaScript and React'."

"Think back to a standout project or achievement at [Company Name]. What was it, and what role did you play? For instance, 'Led the development of a new customer feedback system that increased user satisfaction by 20%'."

"Are/Were you in charge of any teams or projects? Describe your leadership role. For example, 'Managed a team of five developers to deliver quarterly software updates'."

"What skills have you honed or acquired while working at [Company Name]? Give me specifics. For example, 'Enhanced my expertise in machine learning and data analysis'."

"Tell me about a significant challenge you faced at [Company Name] and how you overcame it. Be detailed. For instance, 'Resolved a critical server outage within 24 hours by coordinating with cross-functional teams'."

"If you have left or plan to leave, why did you decide to leave [Company Name]? What prompted the change? (Skip if currently employed without plans to leave.)"

**If Education:**

"Alright, tell me the name of the institution and the degree or certification you earned. For example, 'MIT, Bachelor of Science in Computer Science'."

"What were the dates of your attendance at [Institution Name]? Provide exact years. For instance, 'September 2010 - June 2014'."

"What was your major or field of study at [Institution Name]? Describe your focus. For example, 'Specialized in Artificial Intelligence and Machine Learning'."

"Were you involved in any extracurricular activities, clubs, or organizations at [Institution Name]? Elaborate. For instance, 'President of the Robotics Club and member of the Debate Team'."

"Did you receive any honors, awards, or scholarships while studying at [Institution Name]? List them. For example, 'Graduated with honors, Dean's List, and recipient of the National Merit Scholarship'."

"What were some key projects or research papers you worked on at [Institution Name]? Detail your involvement. For instance, 'Developed a thesis on neural network optimization that was published in a peer-reviewed journal'."

"How has your education at [Institution Name] influenced your career choices and professional development? For example, 'My coursework in AI directly led to my current role as a data scientist'."

**Final Synthesis for Work Experience:**

"Based on the information you've provided, let’s draft a concise description for your role at [Company Name] in 4-5 sentences. Highlight your responsibilities, achievements, and impact. For example, 'As a Software Engineer at Google since 2015, I developed web applications using JavaScript and React. I led a project that increased user satisfaction by 20%. I enhanced my skills in machine learning and data analysis, contributing significantly to our team’s success.'"

**Final Synthesis for Education:**

"Based on the information you've provided, let’s draft a concise description of your education at [Institution Name] in 4-5 sentences. Highlight your studies, achievements, and how they prepared you for your career. For example, 'I earned a Bachelor of Science in Computer Science from MIT, specializing in AI and Machine Learning. During my studies from 2010 to 2014, I led the Robotics Club and made the Dean's List. My thesis on neural network optimization was published in a peer-reviewed journal, laying the foundation for my career as a data scientist.'"

The prompt will initiate ChatGPT to ask you a series of questions. Answer all of them, and you'll get a tailored description of each of your jobs and academic roles. Here's my LinkedIn profile's Work Experience section after updating it with the ChatGPT-generated descriptions.

Following the same idea, you can also update the Education section.

The quality of generated descriptions will improve with the paid version of ChatGPT. However, for the sake of this tutorial, I did use the free version and made some minor edits to the output in terms of grammar and phrasing.

You might've heard, "A picture is worth a thousand words," and we are trying to exemplify that with the LinkedIn Profile Background Image. The idea is to include a photo or image that encapsulates and complements your LinkedIn profile, so profile visitors can quickly get an idea of who you are as a professional. You can check out this Ultimate Guide to Crafting the Perfect LinkedIn Cover Image for ideas and inspiration on how to make this image.

Now, Canva is arguably the most popular tool for creating these Profile Background Images. It has tons of useful templates that you can edit and optimize to build these images.

However, since these are premade templates, other people will also be using them. As such, I'll be using AI to ensure we are using something personalized and representative of our unique professional background.

First, let's jump on over to Claude (the free version will suffice) and enter the following prompt:

Analyze the following points and generate 10 ideas for a LinkedIn Profile Background image that I can potentially use a text-to-image AI tool to generate:

Headline: "[Insert your headline here]"

Professional Summary: "[Insert your professional summary or about section here]"

Work Experience: "[List your work experience here, including roles, responsibilities, and key achievements]"

Education Experience: "[List your education experience here, including degrees, institutions, and notable accomplishments]"

You can also use this prompt with ChatGPT. But I personally find Claude's output better for creativity-oriented workloads .

Here's a look at some of the results I got:

Now remember that you can prompt Claude to generate 10 more ideas if you don't like the ones you got first. Also, you can mix and match elements you like from each of these ideas to form your own prompt. I particularly like this idea:

A futuristic, minimalistic design featuring abstract shapes and lines drawing the outline of tech gadgets, books, and writing tools. High contrast. No images, just lines.

After picking an idea to use as your prompt, head on over to the text-to-image model of your preference. I'll be using OpenArt (free trial) for this showcase. And here's how the image turned out:

Once you have the image ready, go to Canva > click on the "Create A Design" button > search for "LinkedIn Background Photo ," and select it.

Now, inside the Canva editor, you simply drag and drop the photo to upload it, reposition it to fit the aspect ratio, and add any text you like—preferably your name and professional title. And that's it, your LinkedIn Background Cover Image is ready. Here's how my LinkedIn profile looked after all the AI tweaks and edits:

Optimizing your LinkedIn profile doesn't have to be a tedious chore. When you find yourself at a loss for words or ideas, use AI tools to break free from creative blocks and enhance your profile effortlessly.