Book Review: Epidemiology Mental Disorders in Canada: An Epidemiological Perspective
Bibliographic record
Abstract
Mental Disorders in Canada: An Epidemiological Perspective John Cairney, David L Streiner, editors. Toronto (ON): University of Toronto Press; 2010. 432 p. CanS37.95 Reviewer rating: Good Epidemiology This edited volume was produced to celebrate contributions of late Alexander Leighton to field of psychiatric epidemiology in Canada. Leighton carried out Stirling County Study, initiating our modem approach to psychiatric epidemiology through community surveys. The aim of book is to provide a of research in psychiatric epidemiology in Canada1'5 and editors contend that no such review currently exists. This work certainly provides a single source for interested readers to capture major epidemiologic research that has been carried out over last 3 decades in Canada. These experts discuss both breakthroughs and blemishes of these previous works. For example, in terms of blemishes, 2002 Canadian Community Health Survey, Cycle 1.2: Mental Health and Weil-Being (CCHS 1 .2) is criticized for choosing to only partially code mental disorders from Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, making it difficult to compare these results with other major surveys. Regarding breakthroughs, same CCHS 1.2 provides first pan-Canadian data on co-occurring mental disorders and substance use problems; specifically, 1 .7% or 435 000 people in Canada suffered from mood or anxiety disorders and substance use problems during 2002, year of survey. The contributors to volume are who's who of Canadian psychiatric epidemiology, including John Cairney, current president of Canadian Academy of Psychiatric Epidemiology; David Streiner, our Canadian guru on measurement and statistics; Roger Bland, executive medical director at Alberta Health Board; Paula Goering, Canadian Institutes of Health Research and Canadian Health Services Research Foundation Chair in health services research; and Isaac Sakinofsky, Canadian expert in epidemiology of suicide, to name but a few of distinguished contributors. The book is organized into 6 parts: context and theory; methodological issues; epidemiology of disorders; special topics, including prevalence of disorders in migrants and among criminal offenders; mental health care services, policy; and final thoughts. Several important issues are raised from this work. The first chapter by Streiner and Cairney gives an interesting brief history of psychiatric epidemiology; I was reading this as country was debating Conservative Government's decision to abolish mandatory long-form census. Streiner and Cairney recall sixth US census conducted in 1840 that attempted to count the insane at a national level. …
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".