Explicit and Implicit Attitudes of Canadian Psychiatrists toward People with Mental Illness
Bibliographic record
Abstract
OBJECTIVE: People with mental illness suffer stigma and discrimination across various contexts, including the health care setting, and clinicians' attitudes play an important role in perpetuating stigma. Effective stigma-reduction interventions for physicians require a better understanding of explicit (that is, conscious and controllable) and implicit (that is, subconscious and automatic) forms of bias, and of predictors and moderators of stigma. METHODS: Members of a Canadian university psychiatry department and of the Canadian Psychiatric Association (CPA) were invited to participate in a web-based study consisting of 2 measures of explicit attitudes, the Social Distance Scale (SDS) and the Opening Minds Scale for Health Care Providers (OMS-HC), and 1 measure of implicit attitudes, the Implicit Association Test (IAT). RESULTS: Thirty-five psychiatry residents and 68 psychiatrists completed the study (response rates of 12.1% for the university sample and 3.3% for the CPA sample). Participants desired greater social distance from the vignette patient with schizophrenia. Mean IAT scores, although negative, did not reach the threshold for a meaningful effect size. Patient contact positively predicted IAT scores, while age, sex, and level of training (resident, compared with psychiatrist) did not. Neither patient contact nor implicit attitudes predicted SDS or OMS-HC scores. CONCLUSION: Psychiatrists did not differ from psychiatry residents on any measures of explicit or implicit attitudes toward mental illness. Explicit attitudes toward people with mental illness were relatively negative; implicit attitudes were neither negative nor positive; and implicit and explicit attitudes were not correlated. Greater patient contact predicted more positive implicit attitudes, but did not predict explicit attitudes.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".