Comparing Mental Health of Francophone Populations in Canada, France, and Belgium: 12-Month Prevalence Rates of Common Mental Disorders (Part 1)
Bibliographic record
Abstract
OBJECTIVE: To compare the 12-month prevalence of common mental disorders among francophones in Canada, France, and Belgium. This is the first article in a 2-part series comparing mental disorders and service use prevalence of French-speaking populations. METHODS: This is a secondary analysis of data from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2) in 2002 and the European Study of Epidemiology of Mental Disorders-Mental Health Disability (ESEMeD) from 2001 to 2003, where comparable questionnaires were administered to representative samples of adults in Canada, France, and Belgium. In Canada, francophone respondents living in Quebec (n = 7571) and outside Quebec (n = 500) completed the French version of the CCHS 1.2 questionnaire. Francophone respondents in Belgium (n = 389) and in France (n = 1436) completed the French version of the ESEMeD population survey questionnaire. Major depressive episodes (MDEs), specific anxiety disorders (ADs), and alcohol abuse and (or) dependence disorders' rates were assessed. RESULTS: The overall prevalence rate for the presence of any MDE, AD, or alcohol abuse and (or) dependence was similar in all francophone populations studied in Canada and Europe and averaged 8.5%. CONCLUSIONS: Mental disorders were equally distributed in all francophone populations studied. Cross-national comparisons continue to be instrumental in providing information useful for the creation of appropriate policies and programs for specific subsets of populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".