Interprofessional education in mental health: An opportunity to reduce mental illness stigma
Bibliographic record
Abstract
Mental illness stigma is a common problem in healthcare students and professionals in addition to the general public. Stigma is associated with numerous negative outcomes and hence there is an urgent need to address it. This article explores the potential for interprofessional education (IPE) to emerge as a strategy to reduce mental illness stigma amongst healthcare students and professionals. Most anti-stigma strategies use a combination of knowledge and contact (with a person with lived experience) to change attitudes towards mental illness. Not surprisingly interprofessional educators are well acquainted with theory and learning approaches for attitude change as they are already used in IPE to address learners' attitudes and perceptions of themselves, other professions, and/or teamwork. This article, through an analysis of IPE pedagogy and learning methods, identifies opportunities to address mental illness stigma with application of the conditions that facilitate stigma reduction. The goal of this article is to raise awareness of the issue of mental illness stigma amongst healthcare students and professionals and to highlight interprofessional education as an untapped opportunity for change.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".