Public Spending on Health Service and Policy Research in Canada, the United Kingdom, and the United States: A Modest Proposal
Bibliographic record
Abstract
Health services and policy research (HSPR) represent a multidisciplinary field which integrates knowledge from health economics, health policy, health technology assessment, epidemiology, political science among other fields, to evaluate decisions in health service delivery. Health service decisions are informed by evidence at the clinical, organizational, and policy level, levels with distinct, managerial drivers. HSPR has an evolving discourse spanning knowledge translation, linkage and exchange between research and decision-maker partners and more recently, implementation science and learning health systems. Local context is important for HSPR and is important in advancing health reform practice. The amounts and configuration of national investment in this field remain important considerations which reflect priority investment areas. The priorities set within this field or research may have greater or lesser effects and promise with respect to modernizing health services in pursuit of better value and better population outcomes. Within Canada an asset map for HSPR was published by the national HSPR research institute. Having estimated publicly-funded research spending in Canada, we sought identify best available comparable estimates from the United States and the United Kingdom. Investments from industry and charitable organizations were not included in these numbers. This commentary explores spending by the United States, Canada, and the United Kingdom on HSPR as a fraction of total public spending on health and the importance of these respective investments in advancing health service performance. Proposals are offered on the merits of common nomenclature and accounting for areas of investigation in pursuit of some comparable way of assessing priority HSPR investments and suggestions for earmarking such investments to total investment in health services spending.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.000 | 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".