Self-Reported Diagnoses of Schizophrenia and Psychotic Disorders May Be Valuable for Monitoring and Surveillance
Bibliographic record
Abstract
OBJECTIVE: To examine whether a plausible estimate of the prevalence of schizophrenia can be obtained with a self-report item in a health survey. METHODS: We estimated a self-reported prevalence of schizophrenia, using a grouped variable for all people who reported schizophrenia or any other psychotic disorder in the Canadian Community Health Survey: Mental Health and Well-Being (n = 36,984). Estimates were stratified according to age, sex, and province of residence. RESULTS: Of survey respondents, 411 (1.1%) reported having schizophrenia or other psychosis, as diagnosed by a health professional; the weighted and adjusted estimate was 0.9% (0.7% to 1.0%). There was no statistical evidence that the prevalence estimates of schizophrenia and other psychosis varied by age, sex, or province of residence. CONCLUSIONS: Additional studies incorporating a gold standard diagnostic interview should be carried out to determine the validity of the approach. However, responses to 2 self-report survey items provide what appears to be a plausible epidemiologic pattern.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".