Do Canada and the United States Differ in Prevalence of Depression and Utilization of Services?
Bibliographic record
Abstract
OBJECTIVE: This study compared the prevalence of depression and the determinants of mental health service use in Canada and the United States. METHODS: The study used data from preliminary analyses of the 2003 Joint Canada/United States Survey of Health, which measured Canadian (N=3,505) and United States (N=5,183) resident ratings of health and health care services. Cross-national comparisons were made for the 12-month prevalence of DSM-IV major depression, 12-month service use for mental health reasons according to the type of professional seen, and determinants of service use. RESULTS: The rates of depression were similar in Canada (8.2%) and the United States (8.7%). However, U.S. respondents without medical insurance were twice as likely as Canadian respondents and U.S. respondents with medical insurance to meet the criteria for depression. Rates of mental health service use did not differ between Canada (10.1%) and the United States (10.6%). In the United States, medical insurance was not a determinant factor of service use. However, U.S. respondents with no medical insurance were more likely than the other two groups to report an unmet need. Also, among those with depression, U.S. respondents with no medical insurance were less likely to use any type of mental health service (36.5%) than U.S. respondents with medical insurance (55.7%) and Canadians (55.7%). Further, a positive correlation between a mental health need and service use was observed in Canada but not for those without medical insurance in the United States. CONCLUSIONS: There was no difference in the prevalence of depression and mental health service use between Canada and the United States. Among those with depression, however, disparities in treatment seeking were found to be associated with medical insurance in the United States. Both Canada and the United States need to improve access to health services for those with mental disorders, and special attention is needed for those without medical insurance in the United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".