Evidence-Informed Deliberative Processes for Health Benefit Package Design – Part II: A Practical Guide
Bibliographic record
Abstract
BACKGROUND: Countries around the world are using health technology assessment (HTA) for health benefit package design. Evidence-informed deliberative processes (EDPs) are a practical and stepwise approach to enhance legitimate health benefit package design based on deliberation between stakeholders to identify, reflect and learn about the meaning and importance of values, informed by evidence on these values. This paper reports on the development of practical guidance on EDPs, while the conceptual framework of EDPs is described in a companion paper. METHODS: The first guide on EDPs (2019) is further developed based on academic knowledge exchange, surveying 27 HTA bodies and 66 experts around the globe, and the implementation of EDPs in several countries. We present the revised steps of EDPs and how selected HTA bodies (in Australia, Brazil, Canada, France, Germany, Scotland, Thailand and the United Kingdom) organize key issues of legitimacy in their processes. This is based on a review of literature via PubMed and HTA bodies' websites. RESULTS: HTA bodies around the globe vary considerable in how they address legitimacy (stakeholder involvement ideally through participation with deliberation; evidence-informed evaluation; transparency; and appeal) in their processes. While there is increased attention for improving legitimacy in decision-making processes, we found that the selected HTA bodies are still lacking or just starting to develop activities in this area. We provide recommendations on how HTA bodies can improve on this. CONCLUSION: The design and implementation of EDPs is in its infancy. We call for a systematic analysis of experiences of a variety of countries, from which general principles on EDPs might subsequently be inferred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.164 | 0.180 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".