Principles for deliberative processes in health technology assessment
Bibliographic record
Abstract
Deliberative processes are a well-established part of health technology assessment (HTA) programs in a number of high- and middle-income countries, and serve to combine complex sets of evidence, perspectives, and values to support open, transparent, and accountable decision making. Nevertheless, there is little documentation and research to inform the development of effective and efficient deliberative processes, and to evaluate their quality. This article summarizes the 2020 HTAi Global Policy Forum (GPF) discussion on deliberative processes in HTA.Through a combination of small and large group discussion and successive rounds of polling, the GPF members reached strong agreement on three core principles for deliberative processes in HTA: transparency, inclusivity, and impartiality. In addition, discussions revealed other important principles, such as respect, reviewability, consistency, and reasonableness, that may supplement the core set. A number of associated supporting actions for each of the principles are also described in order to make each principle realizable in a given HTA setting. The relative importance of the principles and actions are context-sensitive and must be considered in light of the political, legislative, and operational factors that may influence the functioning of any particular HTA environment within which the deliberative process is situated. The paper ends with suggested concrete next steps that HTA agencies, researchers, and stakeholders might take to move the field forward. The proposed principles and actions, and the next steps, provide a springboard for further research and better documentation of important aspects of deliberation that have historically been infrequently studied.
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 | 0.396 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.016 | 0.137 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.028 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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".