Workplace Antistigma Programs at the Mental Health Commission of Canada: Part 1. Processes and Projects
Bibliographic record
Abstract
The Opening Minds Initiative of the Mental Health Commission of Canada has taken a novel approach to reducing the stigma of mental illness by targeting specific sectors. This first article describes Opening Minds' research and programming initiatives in the workplace target group. This article describes the context of mental illness stigma in Canada and the development of the Opening Minds initiative of the Mental Health Commission of Canada, with a specific focus on the workplace sector. We outline the steps that were taken to develop an evidence-based approach to stigma reduction in the workplace, including reviews of the state of the art in this workplace antistigma programming, as well as the development of tools and measures to assess mental illness stigma in the workplace. Finally, 2 specific program examples (e.g., Road to Mental Readiness and The Working Mind) are used to highlight some of the procedural and logistical learnings for implementing antistigma and mental health initiatives within the workplace. In a second related article, we further examine the Opening Minds workplace initiative, with a discussion of the lessons learned from the implementation and evaluation of antistigma programming in the workplace.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".