Guest Editorial Psychiatric Epidemiology in Canada and the CCHS Study
Bibliographic record
Abstract
he field of psychiatric epidemiology has a long, albeit often neglected, history—one that antedates the begin-ning of psychoanalysis. Klerman (1) and Weissman (2) have outlined 5 “generations ” of epidemiologic research. The first dates back to 1885, when Jarvis (3) used hospital records and key informants in a Massachusetts town to determine the prevalence of treated and untreated mental disorders. The post–World War II era, which Weissman and Klerman both referred to as the golden age of psychiatric epidemiology, saw the famous Midtown Manhattan Study (4) in the US and the Stirling County Study in Canada (5). These studies and others were marked by large representative samples, high response rates, and the use of measures of overall impairment rather than the unreliable psychiatric diagnoses of the day. The third era saw the introduction of structured clinical inter-views, such the Present Status Schedule (6) in the US and the Present State Examination (7) in the UK; the Schedule of
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".