Service Use for Mental Health Reasons: Cross-Provincial Differences in Rates, Determinants, and Equity of Access
Bibliographic record
Abstract
OBJECTIVES: In 2002, Canada undertook its first national survey on mental health and well-being, including detailed questioning on service use. Mental disorders may affect more than 1 person in 5, according to past regional and less comprehensive mental health surveys in Canada, and most do not seek help. Individual determinants play a role in health resource use for mental health (MH) reasons. This study aimed to provide prevalence rates of health care service use for MH reasons by province and according to service type and to examine determinants of MH service use in Canada and across provinces. METHODS: We assessed the prevalence rate (95% confidence interval [CI]) of past-year health service use for MH reasons, and we assessed potential determinants cross-sectionally, using data collected from the Statistics Canada Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). We estimated models of resource use with logistic regression (using odds ratios and 95%CIs). RESULTS: The prevalence of health service use for MH reasons in Canada was 9.5% (95%CI, 9.1% to 10.0%). The highest rates, on average, were observed in Nova Scotia (11.3%; 95%CI, 9.6% to 13.0%) and British Columbia (11.3%; 95%CI, 10.1% to 12.6%). The lowest rates were observed in Newfoundland and Labrador (6.7%; 95%CI, 5.3% to 8.0%) and Prince Edward Island (7.5%; 95%CI, 5.8% to 9.3%). In Canada, the general medical system was the most used for MH reasons (5.4%; 95%CI, 5.1% to 5.8%) and the voluntary network sector was the least used (1.9%; 95%CI, 1.7% to 2.1%). No difference was observed in the rate of service use between specialty MH (3.5%; 95%CI, 3.2% to 3.8%) and other professional providers (4.0%; 95%CI, 3.7% to 4.3%). In multivariate analyses, after adjusting for age and sex, the presence of a mental disorder was a consistent predictor of health service use for MH across the provinces. CONCLUSIONS: There is up to a twofold difference in the type of service used for MH reasons across provinces. The primary care general medical system is the most widely used service for MH. Need remains the strongest predictor of use, especially when a mental disorder is present. Barriers to access, such as income, were not identified in all provinces. Different sociodemographic variables played a role in service seeking within each province. This suggests different attitudes toward common mental disorders and toward care seeking among the provinces.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".