Health Services Research and Government’s Spending on Healthcare Programs: A Welcome Misalignment?
Bibliographic record
Abstract
Excluding capital projects, spending on hospitals, physicians and drugs makes up more than two thirds of provincial and territorial governments' healthcare spending (CIHI 2019).One expects that health services and policy research would be aligned with where the money flows and yet, there is a misalignment.For example, research as published by Healthcare Policy, is not so neatly aligned with provincial and territorial governments' healthcare spending patterns.In this issue, for instance, there are only two such articles -one related to medication adherence and cost, and another related to payment policy associated with a hospital' s alternative level of care utilization.The previous issue of Healthcare Policy was similarly focused, with only two articles the study settings of which were primary care.What does this misalignment mean?Is something important missing between research and governments' emphases?There are several possible reasons for the mismatch that bear careful evaluation: Canadian hospital-, physician-and drug-related health services and policy research is being published elsewhere; these three sectors are not as attractive to researchers because there is little policy movement in them; provincial and territorial governments have not aligned their research imperatives with local research centres very well; or research funders -including the Canadian Institutes of Health Research -are not aligning their funded projects with the governments' policy imperatives or programs.Some or all of these reasons may be true.Irrespective of the causal factors associated with applied health services and policy research outputs, one thing is certain: contemporaneous topics being generated by the Canadian health services research community are conclusively oriented in directions other than hospitals, physicians or drugs.Provinces' and territories' recent initiatives on integrating care, strengthening allied sectors and between-sector innovations are rightly drawing attention from researchers.This activity is now paying dividends to government decision makers and policy makers with Health Services Research and Government' s Spending on Healthcare Programs:A Welcome Misalignment?
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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 | 0.029 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.034 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".