A Mixed Methods Study of Perceptions of Mental Illness and Self-Disclosure of Mental Illness Among Medical Learners
Bibliographic record
Abstract
Introduction: Mental illness stigma remains rooted within medical education and healthcare. We sought to measure perceptions toward mental illness and explore perceptions of self-disclosure of mental illness in medical learners. Method: In a mixed-methods, sequential design, authors recruited medical learners from across Canada. Quantitative data included the Opening Minds Scale for Healthcare providers (OMS-HC), the Self Stigma of Mental Illness Scale (SSMIS), and a wellbeing measure. Qualitative data included semi-structured interviews, which were collected and analyzed using a phenomenological approach. Results: N = 125 medical learners (n = 67 medical students, n = 58 resident physicians) responded to our survey, and N = 13 participants who identified as having a mental illness participated in interviews (n = 10 medical students, n = 3 resident physicians). OMS-HC scores showed resident physicians had more negative attitudes towards mental illness and disclosure (47.7 vs. 44.3, P = 0.02). Self-disclosure was modulated by the degree of intersectional vulnerability of the learner’s identity. When looking at self-disclosure, people who identified as men had more negative attitudes than people who identified as women (17.8 vs 16.1, P = 0.01) on the OMS-HC. Racially minoritized learners scored higher on self-stigma on the SSMIS (Geometric mean: 11.0 vs 8.8, P = 0.03). Interview data suggested that disclosure was fraught with tensions but perceived as having a positive outcome. Discussion: Mental illness stigma and the individual process of disclosure are complex issues in medical education. Disclosure appeared to become more challenging over time due to the internalization of negative attitudes about mental illness.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".