Opening Minds: The Mental Health Commission of Canada’s Anti-Stigma Initiative: Stigma in Canada: Results From a Rapid Response Survey
Bibliographic record
Abstract
Contemporary disability discourse, which has culminated in the United Nations Convention on the Rights of Persons with Disabilities,1 recognizes that social environments create disability through discrimination, social oppression, and social inequity. Signatories to the Convention (and Canada is one) agree to undertake activities to remove harmful stereotypes, prejudices, and discriminatory practices.2 In response to this and the growing public health interest in mental illness–related stigma, many countries have mounted large anti-stigma efforts. Demonstrating the effectiveness of these programs is challenging and requires population-based data. Statistics Canada regularly collects population-based data to monitor the health status and mental health status of Canadians, but has not had detailed information on stigma. In 2008, members of the OM Anti-Stigma Initiative and Statistics Canada addressed this gap. Our paper presents findings from the first national survey of stigma. Empirical objectives are 1) to describe Canadian’s expectations that people will devalue and discriminate against someone with depression, providing a community-based index of stigmatization, and 2) to explore the relation between the experience of being stigmatized among people who have been treated for a mental illness, and selected sociodemographic and mental health-related variables.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".