Welfare generosity and population health among Canadian provinces: a time-series cross-sectional analysis, 1989–2009
Bibliographic record
Abstract
BACKGROUND: Recent work in comparative social epidemiology uses an expenditures approach to examine the link between welfare states and population health. More work is needed that examines the impact of disaggregated expenditures within nations. This study takes advantage of provincial differences within Canada to examine the effects of subnational expenditures and a provincial welfare generosity index on population health. METHODS: Time-series cross-sectional data are retrieved from the Canadian Socio-Economic Information Management System II Tables for 1989-2009 (10 provinces and 21 years=210 cases). Expenditures are measured using 20 disaggregated indicators, total expenditures and a provincial welfare generosity index, a ombined measure of significant predictors. Health is measured as total, male and female age-standardised mortality rates per 1000 deaths. Estimation techniques include the Prais-Winsten regressions with panel-corrected SEs, a first-order autocorrelation correction model, and fixed-unit effects, adjusted for alternative factors. RESULTS: Analyses reveal that four expenditures effectively reduce mortality rates: medical care, preventive care, other social services and postsecondary education. The provincial welfare generosity index has even larger effects. For an SD increase in the provincial welfare generosity index, total mortality rates are expected to decline by 0.44 SDs. Standardised effects are larger for women (β=-0.57, z(19)=-5.70, p<0.01) than for men (β=-0.38, z(19)=-5.59, p<0.01). CONCLUSIONS: Findings show that the expenditures approach can be effectively applied within the context of Canadian provinces, and that targeted spending on health, social services and education has salutary effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".