Effectiveness of contact-based education for reducing mental illness-related stigma in pharmacy students
Bibliographic record
Abstract
BACKGROUND: A strategy for reducing mental illness-related stigma in health-profession students is to include contact-based sessions in their educational curricula. In such sessions students are able to interact socially with a person that has a mental illness. We sought to evaluate the effectiveness of this strategy in a multi-centre study of pharmacy students. METHODS: The study was a randomized controlled trial conducted at three sites. Because it was necessary that all students receive the contact-based sessions, the students were randomized either to an early or late intervention, with the late intervention group not having participated in the contact-based education at the time when the primary outcome was assessed. The primary outcome, stigma, was assessed using an attitudes scale called the Opening Minds Survey for Health Care Providers (OMS-HC). RESULTS: We initially confirmed that outcomes were homogeneous across study centres, centre by group interaction, p = 0.76. The results were pooled across the three study centres. A significant reduction in stigma was observed in association with the contact-based sessions (mean change 4.3 versus 1.5, t=2.1, p=0.04). The effect size (Cohen's d) was 0.45. A similar reduction was seen in the control group when they later received the intervention. CONCLUSIONS: Contact-based education is an effective method of reducing stigma during pharmacy education. These results add to a growing literature confirming the effectiveness of contact-based strategies for stigma reduction in health profession trainees.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".