Are There Mental Health Differences between Francophone and Non-Francophone Populations in Manitoba?
Bibliographic record
Abstract
OBJECTIVE: Francophones may experience poorer health due to social status, cultural differences in lifestyle and attitudes, and language barriers to health care. Our study sought to compare mental health indicators between Francophones and non-Francophones living in the province of Manitoba. METHODS: Two populations were used: one from administrative datasets housed at the Manitoba Centre for Health Policy and the other from representative survey samples. The administrative datasets contained data from physician billings, hospitalizations, prescription drug use, education, and social services use, and surveys included indicators on language variables and on self-rated health. RESULTS: Outside urban areas, Francophones had lower rates of diagnosed substance use disorder (rate ratio [RR] = 0.80; 95% CI 0.68 to 0.95) and of suicide and suicide attempts (RR = 0.59; 95% CI 0.43 to 0.79), compared with non-Francophones, but no differences were found between the groups across the province in rates of diagnosed mood disorders, anxiety disorders, dementia, or any mental disorders after adjusting for age, sex, and geographic area. When surveyed, Francophones were less likely than non-Francophones to report that their mental health was excellent, very good, or good (66.9%, compared with 74.2%). CONCLUSIONS: The discrepancy in how Francophones view their mental health and their rates of diagnosed mental disorders may be related to health seeking behaviours in the Francophone population. Community and government agencies should try to improve the mental health of this population through mental health promotion and by addressing language and cultural barriers to health services.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".