Health Services Research Spending and Healthcare System Impact Comment on "Public Spending on Health Service and Policy Research in Canada, the United Kingdom, and the United States: A Modest Proposal"
Bibliographic record
Abstract
The challenges associated with translating health services and policy research (HSPR) evidence into practice are many and long-standing. Indeed, those challenges have themselves spawned new areas of research, including knowledge translation and implementation science. These sub-disciplines have increased our understanding of the critical success factors associated with the uptake of research evidence into (system) practice. Engaging those for whom research evidence is likely to help solve implementation and/or policy problems, and ensuring that they are key partners throughout the research life-cycle, appear to us (based on current evidence) to be the most direct and effective paths to improved knowledge translation. In that regard, building on Canada's recent Strategy for Patient Oriented Research (SPOR) would seem to offer considerable promise. The "modest" proposals offered by Thakkar and Sullivan seem less likely to bear fruit.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.082 | 0.056 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".