A Model for Priority Setting in Health Technology Innovation Policy
Bibliographic record
Abstract
Health Technology Assessment focuses on equal appraisal of health technologies introduced into the market. This has made regulators and the governance of innovation reactive and dependent on the initiatives innovators take for technology development, thus making it supply driven. The policy makers’ role has become one of appraising technologies that are already developed rather than guiding the development agenda. This severely limits the possibility to ensure that health technologies sufficiently address major issues such as burden of disease, trade deficit and health inequalities. It places governments outside of the actor arena that co-shapes technologies in the early stages, restricting the involvement to facilitating scale up or not. It makes it hard to achieve health technology governance practices that maximally contribute to ensure technological developments that actually address public concerns. What is the potential of frameworks for changing this dynamics and how can evidence shape technology development agenda’s without falling into the traps of regulator lock-in or social engineering? The methodology presented in this study takes first but important steps towards an evidence based framework for priority setting to guide innovations, particularly in health and social sectors
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".