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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".