INSIGHTS FROM THE FRONT LINES: A COLLECTION OF STORIES OF HTA IMPACT FROM INAHTA MEMBER AGENCIES
Bibliographic record
Abstract
This mini-theme contains six stories of health technology assessment (HTA) impact from member agencies of The International Network of Agencies for Health Technology Assessment (INAHTA), which were originally shared at the 2015 and 2016 INAHTA Congresses. The INAHTA impact story sharing is an innovative network activity where member agency representatives share experiences of HTA impact in a loosely structured story format. Through this process, members gain insights from other agencies on new ways of thinking about and approaching HTA impact assessment. A guide is provided to members to prepare their story, and the best story receives the David Hailey Award for Best Impact Story. This mini-theme contains stories of HTA impact from six member agencies in different parts of the world: the Health Assessment Division of the Ministry of Public Health (Uruguay), the Institute of Quality and Efficiency in Health Care (Germany), the Health Information and Quality Authority (Ireland), the Finnish Office for Health Technology Assessment (Finland), the Australian Safety and Efficacy Register of New Interventional Procedures-Surgical (Australia), and the Institut national d'excellence en santé et en services sociaux (Canada). Across the papers, common themes emerge about the importance of appropriate engagement of stakeholders and the broadening scope of HTA beyond reimbursement 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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".