De “myth” ifying Mental Health – Findings from a Community University Research Alliance (CURA)
Bibliographic record
Abstract
Many myths exist regarding mental illness and those with mental health issues. Under the auspices of a Community-University Research Alliance on Housing and Mental Health, a partnership between academics, community health and social service agencies and representatives of consumer-survivor groups, fourteen consumer-survivor and eight family member focus groups were held throughout Southwestern Ontario. Individual interviews were also conducted with 150 male and 150 female community-based mental health system consumer-survivors living in a variety of housing environments in London, Ontario. The findings dispute beliefs around four myths: that people with mental health problems are a homogenous population, which was highlighted by significant differences between men and women in frequency and length of psychiatric hospitalizations, primary diagnosis, problem severity, psychoactive drug use and sexual abuse, are unemployed because they are uneducated, are violent and dangerous and thus spend extended periods of time incarcerated and are unsupported by their families which then leads to housing problems. Challenging these and other equally erroneous myths is essential in responding to the oppression faced by mental-health consumer-survivors and in developing a national strategy for mental health.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".