Using Administrative Data to Analyze the Prevalence and Distribution of Schizophrenic Disorders
Bibliographic record
Abstract
OBJECTIVE: In order to effectively plan and implement psychiatric services, a clear estimate of the prevalence and distribution of the population in need is required. The authors examined the use of administrative data as a means of estimating the prevalence and distribution of schizophrenic disorders. METHODS: Administrative health services data for residents of the Canadian province of British Columbia in the age range 15 to 65 years (total population in 1997-1998 of 2,703,588) were examined over a three-year period. Potential cases of schizophrenic disorder were identified on the basis of the presence of a diagnostic code of 295 in one or more of three databases. One-year prevalence rates were estimated for each of the province's geographic regions, and associations with low income and unemployment were examined. RESULTS: One-year prevalence rate estimates were.45 cases per 100 population in 1996-1997 and 1997-1998 and.42 cases per 100 in 1998-1999. The prevalence estimates of all 88 local health areas in the province were consistent across the three-year period; Pearson correlations were determined to be approximately.9. One-year contact prevalence rates for schizophrenic disorders were significantly correlated in all three years to the percentage of persons with low income in the individual geographic regions but were not correlated with unemployment rates. CONCLUSIONS: In areas with well-developed health services, analyses of administrative data appear to provide cost-effective means of examining the prevalence and distribution of schizophrenic disorders.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".