Making Research Matter 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
We offer a UK-based commentary on the recent "Perspective" published in IJHPM by Thakkar and Sullivan. We are sympathetic to the authors' call for increased funding for health service and policy research (HSPR). However, we point out that increasing that investment - in any of the three countries they compare: Canada, the United States and the United Kingdom- will ipso facto not necessarily lead to any better use of research by health system decision-makers in these settings. We cite previous authors' descriptions of the many factors that tend to make the worlds of researchers and decision-makers into "two solitudes." And we call for changes in the structure and funding of HSPR, particularly the incentives now in place for purely academic publishing, to tackle a widespread reality: most published research in HSPR, as in other applied fields of science, is never read or used by the vast majority of decision-makers, working out in the "real world.
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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 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.140 | 0.112 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".