Service Use and Unmet Needs for Substance Use and Mental Disorders in Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate patterns and predictors of help seeking and met/unmet needs for mental health care in a national population health survey. METHOD: Participants were respondents to the 2012 Canadian Community Health Survey on Mental Health (CCHS-MH; n = 25,133). We used regression to identify the diagnostic and sociodemographic predictors of the use of informal supports, primary care, and specialist care, as well as perceived unmet needs. RESULTS: Eleven percent of Canadians reported using professionally led services for mental health or substance use in 2012, while another 9% received informal supports. Two-thirds of people with substance use disorders did not receive any care, and among those who did, informal supports were most common. Seventy-four percent of people with mood/anxiety disorders and 88% of those with co-occurring disorders did access services, most commonly specialist mental health care. Men, older people, members of ethnocultural minorities, those not born in Canada, those with lower education, and those with higher incomes were less likely to receive care. Unmet needs were higher among people with substance use disorders. CONCLUSIONS: Gaps in services continue to exist for some potentially vulnerable population subgroups. Policy and practice solutions are needed to address these unmet needs. In particular, the convergence of research pointing to gaps in the availability and accessibility of high-quality services for substance use in Canada demands attention.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".