Application of evidence-informed deliberative processes in health technology assessment in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVES: Evidence-informed deliberative processes (EDPs) were introduced to guide health technology assessment (HTA) agencies to improve their processes toward more legitimate decision making. A survey among members of the International Network of Agencies for HTA (INAHTA) showed that EDPs can also be relevant for countries that have not (yet) established such an agency. Therefore, we explored to what extent low- and middle-income countries (LMIC) applied the steps and elements stipulated in the EDP framework and their need for guidance. METHODS: The survey among INAHTA members was slightly adapted to address LMIC context and sent to 416 experts identified through several HTA sources. The questions focused on contextual factors and the EDP steps (installation of an appraisal committee, selecting technologies and criteria, assessment, appraisal, communication and appeal). Data collection took place between 21 May and 1 September 2019. Descriptive statistics and qualitative analyses were used to summarize the findings. RESULTS: We received sixty-six meaningful responses from experts in thirty-two LMIC. We found that contextual factors to support HTA development are overall not present or only present to some extent. Respondents indicated that guidance was needed for specific elements related to selecting technologies and criteria, assessment, appraisal, as well as communication and appeal. CONCLUSIONS: EDPs have the potential to provide steps for improving HTA processes. The results of this study can serve as a baseline measurement for future monitoring and evaluation of EDP application in the responding LMIC. This could support the countries in improving their processes and enhancing legitimate decision making when using HTA.
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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 | 0.508 | 0.538 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".