Implementing evidence-informed deliberative processes in health technology assessment: a low income country perspective
Bibliographic record
Abstract
The purpose of this paper is to discuss the potential feasibility and utility of evidence-informed deliberative processes (EPDs) in low income country (LIC) contexts. EDPs are implemented in high and middle income countries and thought to improve the quality, consistency, and transparency of decisions informed by health technology assessment (HTA). Together these would ultimately improve the legitimacy of any decision making process. We argue-based on our previous work and in light of the priority setting literature-that EDPs are relevant and feasible within LICs. The extreme lack of resources necessitates making tough decisions which may mean depriving populations of potentially valuable health technologies. It is critical that the decisions and the decision making bodies are perceived as fair and legitimate by the people that are most affected by the decisions. EDPs are well aligned with the political infrastructure in some LICs, which encourages public participation in decision making. Furthermore, many countries are committed to evidence-informed decision making. However, the application of EDPs may be hampered by the limited availability of evidence of good quality, lack of interest in transparency and accountability (in some LICs), limited capacity to conduct HTA, as well as limited time and financial resources to invest in a deliberative process. While EDPs would potentially benefit many LICs, mitigating the identified potential barriers would strengthen their applicability. We believe that implementation studies in LICs, documenting the contextualized enablers and barriers will facilitate the development of context specific improvement strategies for EDPs.
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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 | 0.169 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.010 | 0.014 |
| 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".