Effect of provincial spending on social services and health care on health outcomes in Canada: an observational longitudinal study
Bibliographic record
Abstract
BACKGROUND: Escalating health care spending is a concern in Western countries, given the lack of evidence of a direct connection between spending and improvements in health. We aimed to determine the association between spending on health care and social programs and health outcomes in Canada. METHODS: We used retrospective data from Canadian provincial expenditure reports, for the period 1981 to 2011, to model the effects of social and health spending (as a ratio, social/health) on potentially avoidable mortality, infant mortality and life expectancy. We used linear regressions, accounting for provincial fixed effects and time, and controlling for confounding variables at the provincial level. RESULTS: A 1-cent increase in social spending per dollar spent on health was associated with a 0.1% (95% confidence interval [CI] 0.04% to 0.16%) decrease in potentially avoidable mortality and a 0.01% (95% CI 0.01% to 0.02%) increase in life expectancy. The ratio had a statistically nonsignificant relationship with infant mortality (p = 0.2). INTERPRETATION: Population-level health outcomes could benefit from a reallocation of government dollars from health to social spending, even if total government spending were left unchanged. This result is consistent with other findings from Canada and the United States.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".