Assessment of clinician well-being and the factors that influence it using validated questionnaires: a systematic review
Bibliographic record
Abstract
Measuring clinician experiences of care and well-being (e.g. job satisfaction, fulfillment) offers insights into the practice environment’s impact, aiding workforce retention, patient safety, and care quality. However, valid measurement instruments are essential. This systematic review identified validated self-reported questionnaires designed to assess clinician well-being and its influencing factors. Psychometric studies in English or French on measurement instruments addressing factors that influence clinician well-being, as proposed by the National Academy of Medicine, were included. Studies published between 2013 and 2023 were retrieved in December 2023 by searching these databases: CINAHL, Embase, HaPI, MEDLINE, PsycINFO, Mental Measurements Yearbook, and APA PsycTests. Study selection was completed by two independent reviewers. Results were summarized narratively, in tables, and figures. Quality of psychometric studies was assessed by the number of measurement properties addressed. The review protocol was registered with INPLASY® (202410047). Out of 10,441 records identified, 136 studies are included. The majority come from the USA (27.2%), Spain (11.0%), Canada (5.9%), or Australia (5.9%). Most focus on instruments for clinicians, regardless of their specialty (55.9%). Among profession-specific instruments (44.1%), nurses and physicians are mainly targeted. The most common domains are: (1) ‘Learning/practice environment’ (38.2%), (2) ‘Healthcare responsibilities’ (21.3%), and (3) ‘Organizational factors’ (19.1%). The most frequently addressed measurement properties are: (1) Internal consistency (88.2%), (2) Structural validity (75.7%), and (3) Content validity (68.4%). Many tools for measuring clinician well-being exist, but few are fully validated. The results of this review provide a foundation to support ongoing psychometric evaluation and cross-cultural adaptation. Audet et al. systematically review the literature aimed at identifying validated self-reported questionnaires designed to assess clinician well-being and its influencing factors. While several tools exist, few have undergone comprehensive validation. Society and healthcare services are evolving rapidly, requiring clinicians to constantly adapt and placing them under continuous pressure. It is essential to investigate the factors influencing their well-being at work to maintain safe and high-quality patient care. To achieve this, valid tools are needed to measure clinician well-being. We conducted a literature review to identify tools currently available worldwide. Our results show that most of these tools are in English and originate from the United States. Moreover, a large proportion of tools focus on physicians and nurses. Given that healthcare organization varies between countries, it is important to have valid tools adapted to each country’s cultural context and language. We therefore identify a need for cross-cultural adaptation of these tools into multiple languages and care settings. Additionally, there should be profession-specific tools for various healthcare providers (e.g., pharmacists, dentists, physiotherapists), not only for physicians and nurses. Improvements to these tools will enable better assessment of health worker wellbeing, which will have a positive impact on them and the patients they treat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".