Opening Minds in Canada: Targeting Change
Bibliographic record
Abstract
OBJECTIVE: To summarize the ongoing activities of the Opening Minds (OM) Anti-Stigma Initiative of the Mental Health Commission of Canada regarding the 4 groups targeted (youth, health care providers, media, and workplaces), highlight some of the key methodological challenges, and review lessons learned. METHOD: The approach used by OM is rooted in community development philosophy, with clearly defined target groups, contact-based education as the central organizing element across interventions, and a strong evaluative component so that best practices can be identified, replicated, and disseminated. Contact-based education occurs when people who have experienced a mental illness share their personal story of recovery and hope. RESULTS: Results have been generally positive. Contact-based education has the capacity to reduce prejudicial attitudes and improve social acceptance of people with a mental illness across various target groups and sectors. Variations in program outcomes have contributed to our understanding of active ingredients. CONCLUSIONS: Contact-based education has become a cornerstone of the OM approach to stigma reduction. A story of hope and recovery told by someone who has experienced a mental illness is powerful and engaging, and a critical ingredient in the fight against stigma. Building partnerships with existing community programs and promoting systematic evaluation using standardized approaches and instruments have contributed to our understanding of best practices in the field of anti-stigma programming. The next challenge will be to scale these up so that they may have a national impact.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".