Regional and Individual Influences on Use of Mental Health Services in Canada
Bibliographic record
Abstract
OBJECTIVE: Knowledge is lacking on the extent to which area-level characteristics contribute to variations observed in the use of mental health services. This study examined the influence of area- and individual-level characteristics on the use of mental health services. METHODS: Data from a nationally representative, population-based, cross-sectional survey, the Canadian Community Health Survey-Mental Health and Well-Being, consisting of adults aged 15 years or older (n = 36 984), were linked to Canadian 2001 Census profiles according to health region boundaries (n = 97). Multilevel multivariable logistic regression modelling was used to: estimate variation in 12-month self-reported use of health services for mental health reasons between health regions; and, estimate the effects of individual- and area-level need, health resources, and sociodemographic factors on self-reported 12-month use of medical services for mental health reasons. RESULTS: There was a 2.1% and 3.5% regional variation for general practitioner-family physician (GP-FP) and psychiatric health service use during 12 months, respectively. Most of the regional variation observed was explained by number of physicians per health region and regional and individual need factors. Adults who were middle-aged, had a post-secondary education, low-income, were separated, widowed, or divorced, and Canadian-born were significantly more likely to use GP-FP and psychiatry services for mental health reasons at the individual level, even after adjusting for area- and individual-level need factors. CONCLUSIONS: Most area-level variation was explained by the availability of health region resources and individual-level need factors. After accounting for need, numerous sociodemographic factors retained their association with use of mental health services. Additional efforts are needed at the area and individual level to reduce inequities through appropriate targeted care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".