The WellNext Scan: Validity evidence of a new team-based tool to map and support physicians’ well-being in the clinical working context
Bibliographic record
Abstract
Occupational well-being is inherent to physicians' professional performance and is indispensable for a cost-effective, robust healthcare system and excellent patient outcomes. Increasing numbers of physicians with symptoms of burnout, depression, and other health issues are demonstrating the need to foster and maintain physicians' well-being. Assessing physicians' well-being, occupational demands, and resources can help create more supportive and health-promoting working environments. The WellNext Scan (WNS) is a 46-item questionnaire developed to assess (i) physicians' well-being and (ii) relevant factors related to physicians' clinical working environment. We collected data to investigate the validity and reliability of the WNS using a non-randomized, multicenter, cross-sectional survey of 467 physicians (staff, residents, doctors not in training, and fellows) from 17 departments in academic and non-academic teaching medical centers in the Netherlands. Exploratory factor analysis detected three composite scales of well-being (energy and work enjoyment, meaning, and patient-related disengagement) and five explanatory factors (supportive team culture, efficiency of practice, job control and team-based well-being practices, resilience, and self-kindness). Pearson's correlations, item-total and inter-scale correlations, and Cronbach's alphas demonstrated good construct validity and internal consistency reliability of the scales (α: 0.67-0.90; item-total correlations: 0.33-0.84; inter-scale correlations: 0.19-0.62). Overall, the WNS appears to yield reliable and valid data and is now available as a supportive tool for meaningful team-based conversations aimed at improving physician well-being.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".