Mental Health Stigma: Explicit and Implicit Attitudes of Canadian Undergraduate Students, Medical School Students, and Psychiatrists
Bibliographic record
Abstract
OBJECTIVES: To compare explicit and implicit stigmatizing attitudes towards mental illness among undergraduate students, medical school students, and psychiatrists, and to assess whether attitudes are associated with education level, exposure to, and personal experience with mental illness. METHODS: Participants from McMaster University were recruited through email. Participants completed a web-based survey consisting of demographics; the Opening Minds Scale for Healthcare Providers (OMS-HC) 12-item survey, which measures explicit stigma; and an Implicit Association Test (IAT), measuring implicit bias toward physical illness (diabetes mellitus) or mental illness (schizophrenia). RESULTS: A total of 538 people participated: undergraduate students ( n = 382), medical school students ( n = 118), and psychiatrists ( n = 38). Psychiatrists had significantly lower explicit and implicit stigma than undergraduate students and medical school students. Having been diagnosed with mental illness or having had a relationship with someone experiencing one was significantly associated with lower explicit stigma. Mean scores on the OMS-HC "disclosure/help-seeking" subscale were higher compared with the "attitudes towards people with mental illness" subscale. There was no correlation between the OMS-HC and IAT. CONCLUSIONS: These findings support the theory that increased education and experience with mental illness are associated with reduced stigma. Attitudes regarding disclosure/help-seeking were more stigmatizing than attitudes towards people with mental illness. The groups identified in this study can potentially benefit from anti-stigma campaigns that focus on reducing specific components of explicit, implicit, public and self-stigma.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".