Disclosure and Double Standards: A Mixed Methods Study of Self-Disclosure of Mental Illness or Addiction Among Medical Learners
Bibliographic record
Abstract
Abstract Purpose Despite the proliferation of initiatives to address wellbeing and reduce burnout, mental illness and addiction stigma remains rooted within medical education and healthcare. One mechanism to address this stigma is self-disclosure. Given the paucity of literature on self-disclosure in medical learners, we sought to explore perceptions of self-disclosure in medical education. Method In a mixed method, convergent triangulation 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), a wellbeing measure, and questions regarding substance use from Statistics Canada. Qualitative data included semi-structured interviews, which were collected and analyzed using a phenomenological approach. Data were collected simultaneously, analyzed separately, and then triangulated. Discrepancies were discussed until consensus was achieved. Results Overall, N= 125 medical learners (n= 67 medical students, n=58 resident physicians) responded to our survey, and N=13 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). Racialized learners scored higher on self-stigma. Interview data suggested that disclosure was fraught with tensions, but perceived as having a positive outcome, including the perception that self-disclosure made learners better physicians and educators in the future. Conclusion The individual process of disclosure is complex and appeared to become more challenging over time due to the internalization of negative attitudes about mental illness. Intersectional vulnerability in medical learners warrants further consideration. Fear of disclosure is an important factor shaped by the learning 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 | 0.035 | 0.040 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".