The Determinants of Public Health Expenditures: Comparing Canada and Spain
Bibliographic record
Abstract
The determinants of public health care expenditure are examined in two of the most decentralized countries in the world (Canada and Spain) for two partly overlapping time-spans of data availability: Canada, 1981 to 2013 and Spain, 2002 to 2013. While Canada generally spends more per capita on health care than Spain, over time Spain’s macro level health indicator performance has surpassed Canada’s. Using regression analysis, we find the key determinants of public health care spending include time trend, income, physician numbers and regional fixed effects. Physician numbers are a significant driver of real per capita public health expenditures in Canada but not Spain despite the greater per capita number of physicians in Spain. Differences in the growth and performance of real per capita income explain much of the gap between public health spending between these two countries with some contribution from differences in per capita physician numbers. The differential health indicator outcomes raise the question of what Canada might do to be more efficient.
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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 | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".