Sociodemographic and Mental Health Predictors of Mental Health Service Use Across Provider Types
Bibliographic record
Abstract
OBJECTIVE: To examine trends in mental health service use across four provider types (family doctors, psychiatrists, psychologists, and social workers) and identify sociodemographic predictors of provider-specific access in Canada. METHODS: This study analyzes seven cycles (2007-2020) of the Canadian Community Health Survey, a nationally representative cross-sectional survey. Trends over time were examined using weighted proportions and counts of service users. Weighted multivariable logistic regression models were applied to the 2019-2020 cycle to assess associations between sociodemographic factors and provider-specific service use. RESULTS: Family doctors were consistently the most accessed providers for mental health concerns, followed by psychologists and social workers, with psychiatrists being least accessed. Psychologist and social worker use increased between 2017 and 2019. In the adjusted regression models (2019-2020), women had higher odds of using family doctors (AOR = 1.21, 95% CI: 1.05-1.39) and social workers (1.19, 1.02-1.40) and lower odds of psychiatrists (AOR = 0.66, 95% CI:0.55-0.79) than men. Adults 65 + had greater odds of family-doctor use (AOR = 4.82, 95% CI: 3.59-6.47) and lower odds of psychologist (AOR = 0.33, 95% CI: 0.24-0.45) and social-worker use (AOR = 0.21, 95% CI:0.16-0.29) than ages 12-17. Post-secondary education (vs less than secondary school) was associated with higher psychologist use (AOR = 1.83, 95% CI 1.12-2.98). Higher income (≥$80,000 vs < $20,000) was associated with lower psychiatrist use (AOR = 0.66, 95% CI 0.50-0.86). Non-Indigenous respondents more often used psychologists (AOR = 1.58, 95% CI: 1.13-2.23), and respondents who are not a visible minority more often used family doctors (AOR = 1.37, 95% CI:1.06-1.77). CONCLUSION: This study reveals a stratified mental health care system in Canada, where sociodemographic factors shape who accesses which providers. While primary care dominates, growth in psychologist and social worker use suggests shifting patterns of engagement. Findings underscore the need for policies that address financial and structural barriers, promote equitable access, and expand coverage for community-based mental health providers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".