INAHTA member agency stories of engaging, adaptable, and impactful HTA
Bibliographic record
Abstract
Health technology assessment (HTA) agencies assess evidence to support decision making about which technologies to provide and pay for in the health system. HTA impact is understood as the influence that HTA report findings can have in the health system, including impacts on reimbursement decisions, changes to health outcomes, or broader system or societal impacts. The International Network of Agencies for Health Technology Assessment (INAHTA) is a global network of publicly funded HTA agencies. INAHTA's mission, in part, is to advance the impact of HTA to support reimbursement decisions and the optimal use of health system resources. Each year, INAHTA awards the David Hailey Award for Best Impact Story to the member agency that shares the best story, as voted by fellow members, about HTA impact. The impact story sharing program in INAHTA contributes to a deeper understanding of what works well (or not so well) in achieving HTA impact. This paper provides six impact stories from agencies that were finalists for the 2021 and 2022 David Hailey Impact Award for Best Impact Story: the Institut national d'excellence en santé et en services sociaux, the Malaysian Health Technology Assessment Section, Ontario Health, the Center for Drug Evaluation, the National Institute for Health and Care Excellence, and Health Technology Wales. These stories demonstrate that HTA agencies can, in differing ways, effectively support governments in their efforts to place evidence at the centre of decision making.
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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 | 0.080 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.039 | 0.021 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.016 | 0.035 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".