Moral harassment and mental health in medical residents: a longitudinal study
Bibliographic record
Abstract
OBJECTIVE: This study investigated whether moral harassment contributes to anxiety, depression, and burnout among medical residents. METHODS: This three-stage longitudinal study involved 218 1st-year residents, of whom 76 (34.9%) participated in all stages. The questionnaire covered demographics, mental health (using the Patient Health Questionnaire-4), burnout (using the Maslach Burnout Inventory Human Services Survey), and harassment experiences. Mental health outcomes and harassment were analyzed using logistic regression. RESULTS: Anxiety and depression scores varied significantly, including a notable decrease in the personal accomplishment dimension of burnout. The prevalence of harassment was above 90%, and most victims were disturbed by the harassment they suffered. While a direct correlation between harassment victimization and reduced mental health was not found, seeking help exacerbated suffering, and depression and emotional exhaustion increased less among surgical residents. CONCLUSION: To the extent of our knowledge, this is the first longitudinal study on mental health and harassment among medical residents. The mental suffering experienced after taking action against harassment suggests that safe environments for addressing these issues are lacking in medical residencies. Further studies concerning surgical residents could shed light on their lower levels of suffering. Institutional changes are needed to support victims and create a healthy environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".