What leads to better health care innovation? Arguments for an integrated policy-oriented research agenda
Bibliographic record
Abstract
This essay is based on the recognition that the current 'downstream' health services research and policy approach to innovation misses the mark on one crucial point. It has not addressed how to promote the design of innovations that are likely to be more valuable than others. Re-visiting the ways in which health services research could inform innovation processes, this paper suggests that three attributes make innovations especially compelling from a health care system perspective: relevance; usability; and sustainability. These could be used as a starting point for outlining a policy-oriented research agenda that could bridge upstream design processes, and downstream needs and priorities. Given the pace at which innovations come about and the complexity of health care systems, we believe that both research and policy should be able to contribute significantly to the shaping of socially valuable technological change in health care. Recognizing that such a long-term goal cannot be reached through a linear, rationalistic process, our paper offers preliminary arguments to start to reconcile the health policy and innovation agendas.
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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 | 0.148 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.036 | 0.056 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.044 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".