How Early-Career Female Physicians Experience Workplace Mental Health and Leaves of Absence In Ontario
Bibliographic record
Abstract
The intersection of gender and early-career stage on the mental health of physicians is emerging and evident. This qualitative, interview-based study explores the perspectives of early-career female physicians regarding their mental health in the context of their work, their experiences with taking a leave of absence from work, and promising practices and supports that can support early-career female physicians in the workplace with regards to mental health and leaves of absence. Nine interviews with female physicians in the first ten years of practice in Ontario were conducted and analyzed thematically. A conceptual framework borrowed from the Healthy Professional Worker (HPW) Partnership was employed and revised based on the findings. The findings suggest that increased awareness of the challenges faced by early-career female physicians may contribute to the destigmatization of mental health and leaves of absence and foster supports at work. Policy makers and regulatory bodies should consider developing equitable leave of absence policies for physicians and reframing how seeking mental health care is viewed to contribute to positive culture change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".