Measuring the Effect of a Large Reduction in Welfare Payments on Mental Health Service Use in Welfare-Dependent Neighborhoods
Bibliographic record
Abstract
BACKGROUND: Major social policy changes were implemented in Canada in the last decade with few efforts to examine their potential health effects. OBJECTIVES: We sought to determine the impact of a large reduction in welfare benefits on use of ambulatory physician mental health services in areas with high levels of welfare dependency relative to areas with low levels of welfare dependency. METHODS: The setting was Toronto, Canada. Data sources included census, provincial health insurance, and municipal welfare data. We used generalized estimating equations to compare ambulatory mental health service rates by neighborhood level of welfare dependency before and after a 21.6% reduction in welfare payments. RESULTS: There were no long-term relative differences by welfare dependency in mental health service use before compared with after the policy change. There was a very small short-term increase in mental health visits to generalists in the 6 months after the policy change. We demonstrated a marked gradient in psychiatric service use with low welfare dependency areas having significantly higher rates of use than high welfare dependency areas. CONCLUSIONS: We demonstrated a mismatch between known levels of need for care and levels of psychiatric use. We conclude that where use of services is not tightly linked to need for services, utilization data may be unsuitable for evaluating programs or policies. Social policy changes with potential health effects should have integrated evaluations planned at the time of policy implementation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".