The pathway from mental health, leaves of absence, and return to work of health professionals: Gender and leadership matter
Bibliographic record
Abstract
Health professions are ranked among the most stressful occupations and have a much higher likelihood of absenteeism from work. In this article, we present findings from four health professional case studies in our Healthy Professional Worker partnership, involving surveys with 1,860 respondents and 163 interviews with nurses, physicians, midwives, and dentists conducted between December 2020 and April 2021. We found that the pathway from mental health experiences through to the decision to take a leave of absence and return to work differed between the health professions and that both gender and leadership matter greatly. There is a need to de-stigmatize mental health issues and encourage greater awareness and support from supervisors and colleagues. Leadership can play an important role in mitigating mental health issues, and as such investment in both leadership training and mentorship are important first steps in acting upon our research findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".