Use of Administrative Data for the Surveillance of Mental Disorders in 5 Provinces
Bibliographic record
Abstract
OBJECTIVE: To evaluate the usefulness of administrative data for the surveillance of mental illness in Canada using databases in the following 5 provinces: British Columbia, Ontario, Quebec, Nova Scotia, and Alberta. METHOD: We used a population-based record-linkage analysis with data from physician billings, hospital discharge abstracts, and community-based clinics. The following diagnostic codes from the International Classification of Diseases, Ninth Edition, were used to define cases: 290 to 319, inclusive. RESULTS: The prevalence of treated psychiatric disorder was similar in Nova Scotia, British Columbia, Alberta, and Ontario at about 15%. The prevalence for Quebec was slightly lower at 12%. Findings from the provinces showed remarkable consistency across age and sex, despite variations in data coding. Women tended to show a higher prevalence overall of treated mental disorders than men. Prevalence increased steadily to middle age, declining in the 50s and 60s, and then increasing again after age 70 years. CONCLUSIONS: Provincial and territorial administrative data can provide a useful, reliable, and economical source of information for the surveillance of treated mental disorders. Such a surveillance system can provide longitudinal data at little cost to support health service provision and planning.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".