Bibliographic record
Abstract
OBJECTIVES: While mental health services provided by general practitioners and psychiatrists can be billed to public health insurance programs in Canada, services provided by psychologists, social workers and other non-physician providers cannot. This study assesses the extent to which access to mental health services varies by income after first taking into account the higher concentration of mental health needs at lower income levels. METHOD: Data from the Canadian Community Health Survey 2013-2014 are used to calculate need-standardized concentration indices for access to mental health services. RESULTS: More pro-rich utilization of mental health services provided by non-physicians and more equitable utilization of physician services is found for psychologists and general practitioners, but not for social workers, nurses and psychiatrists. Unmet need for healthcare for mental health problems is found to be more pro-poor than unmet need for physical health problems. CONCLUSION: By standardizing for inequitable distribution of mental health need, this study provides strong evidence that income-based inequity in access to mental health services is an issue under Canada's two-tier system, particularly with regard to general practitioners and psychologists. For other types of providers, the results suggest that inequities in service utilization vary not just by Medicare coverage but also by service settings and target populations. Despite these variations, greater inequities in unmet need for mental health care than for physical health care suggest that inequity is the dominant reality for Canadians. The results provide a baseline that could be used to assess the equity impacts of policy reforms.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".