Deliberative processes in decision making informed by health technology assessment in Latin America
Bibliographic record
Abstract
OBJECTIVE: The objective of Health Technology Assessment International's 6th Latin America Policy Form, held in 2021, was to explore the implementation of deliberative processes in the framework of health technology assessment (HTA) and how agencies in the region could involve stakeholders in this process. METHODS: This paper is based on a preparatory survey, a background document, and the deliberative work of participants at the virtual Forum conducted in 2021. There were ninety-one participants in the open session and fifty-two in the closed sessions, representing twelve countries and diverse areas of the health sector. RESULTS: While there are mechanisms in most countries in Latin America to consider stakeholder involvement to some degree, it remains reduced or limited to a consultative role, making true participative involvement rare. There are significant barriers and structural and contextual limitations that have impeded or slowed progress toward deliberative processes. Relatively low levels of institutionalization and knowledge about HTA, as well as the lack of trust among stakeholders are important challenges. This situation has impacted health systems by diminishing the legitimacy of decisions and the very structures and processes of HTA. CONCLUSION: The Forum's broad group of participants identified barriers, facilitators, and recommendations to improve the use of deliberative processes in Latin America to foster improved fairness and reasonableness in HTA and 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 | 0.200 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".