HEALTH TECHNOLOGY ASSESSMENT FOR DECISION MAKING IN LATIN AMERICA: GOOD PRACTICE PRINCIPLES
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to identify good practice principles for health technology assessment (HTA) that are the most relevant and of highest priority for application in Latin America and to identify potential barriers to their implementation in the region. METHODS: HTA good practice principles proposed at the international level were identified and then explored during a deliberative process in a forum of assessors, funders, and product manufacturers. RESULTS: Forty-two representatives from ten Latin American countries participated. Good practice principles proposed at the international level were considered valid and potentially relevant to Latin America. Five principles were identified as priority and with the greatest potential to be strengthened at this time: transparency in the production of HTA, involvement of relevant stakeholders in the HTA process, mechanisms to appeal decisions, clear priority-setting processes in HTA, and a clear link between HTA and decision making. The main challenge identified was to find a balance between the application of these principles and the available resources in a way that would not detract from the production of reports and adaptation to the needs of decision makers. CONCLUSIONS: The main recommendation was to progress gradually in strengthening HTA and its link to decision making by developing appropriate processes for each country, without trying to impose, in the short-term, standards taken from examples at the international level without adequate adaptation of these to local contexts.
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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 | 0.326 | 0.234 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.031 | 0.015 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.015 | 0.020 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".