Stigma in Canada: Results from a Rapid Response Survey
Bibliographic record
Abstract
OBJECTIVE: Our paper presents findings from the first population survey of stigma in Canada using a new measure of stigma. Empirical objectives are to provide a descriptive profile of Canadian's expectations that people will devalue and discriminate against someone with depression, and to explore the relation between experiences of being stigmatized in the year prior to the survey among people having been treated for a mental illness with a selected number of sociodemographic and mental health-related variables. METHOD: Data were collected by Statistics Canada using a rapid response format on a representative sample of Canadians (n = 10 389) during May and June of 2010. Public expectations of stigma and personal experiences of stigma in the subgroup receiving treatment for a mental illness were measured. RESULTS: Over one-half of the sample endorsed 1 or more of the devaluation discrimination items, indicating that they believed Canadians would stigmatize someone with depression. The item most frequently endorsed concerned employers not considering an application from someone who has had depression. Over one-third of people who had received treatment in the year prior to the survey reported discrimination in 1 or more life domains. Experiences of discrimination were strongly associated with perceptions that Canadians would devalue someone with depression, younger age (12 to 15 years), and self-reported poor general mental health. CONCLUSIONS: The Mental Health Experiences Module reflects an important partnership between 2 national organizations that will help Canada fulfill its monitoring obligations under the United Nations Convention on the Rights of Persons with Disabilities and provide a legacy to researchers and policy-makers who are interested in monitoring changes in stigma over time.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".