Bibliographic record
Abstract
BACKGROUND: This article assesses the association between self-rated mental health and selected World Mental Health-Composite International Diagnostic Interview (WMH-CIDI)-measured disorders, self-reported diagnoses of mental disorders, and psychological distress in the Canadian population. DATA AND METHODS: Data are from the 2002 Canadian Community Health Survey: Mental Health and Well-being. Weighted frequencies and cross-tabulations were used to estimate the prevalence of each mental morbidity measure and self-rated mental health by selected characteristics. Mean self-rated mental health scores were calculated for each mental morbidity measure. The association between self-rated mental health and each mental morbidity measure was analysed with logistic regression models. RESULTS: In 2002, an estimated 1.7 million Canadians aged 15 or older (7%) rated their mental health as fair or poor. Respondents classified with mental morbidity consistently reported lower mean self-rated mental health (SRMH) and had significantly higher odds of reporting fair/poor mental health than did those not classified with mental morbidity. Gradients in mean SRMH scores and odds of reporting fair/poor mental health by recency of WMH-CIDI-measured mental disorders were apparent. A sizeable percentage of respondents classified as having a mental morbidity did not perceive their mental health as fair/poor. INTERPRETATION: Although self-rated mental health is not a substitute for specific mental health measures it is potentially useful for monitoring general 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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".