Interpreting forty-three-year trends of expenditures on public health in Canada: Long-run trends, temporal periods, and data differences
Bibliographic record
Abstract
The COVID-19 pandemic has raised concerns around public health (PH) investments. Among OECD countries, Canada devotes one of the largest shares of total health expenditures to PH. Examining retrospectively PH spending growth over a very long period may hold lessons on how to reach this high share. Further, different historical periods can be used to understand how macroeconomic conditions affect PH spending growth. Using forty-three years of data, we examine real PH spending growth per capita, comparatively between thirteen Canadian jurisdictions and with other key publicly funded healthcare sectors (physicians, hospitals, and pharmaceuticals), as well as by four periods defined by macroeconomic conditions. We find a five-fold increase on average in PH spending since 1975, a growth above physicians and hospitals, but below pharmaceuticals. However, there is substantial variation in PH growth between periods and across the country. Because concerns have been raised over PH spending data in other OECD countries, we explore differences between spending estimates reported by the national agency and ten provincial budgetary estimates, and find the former is larger. The magnitude of the difference varies between jurisdictions but not much over time. Although these differences do not challenge the presence of growth in PH spending, they show that the growth may be below that of hospitals. A better categorization of PH financing data is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".