TRANSFERABILITY OF HEALTH TECHNOLOGY ASSESSMENT REPORTS IN LATIN AMERICA: AN EXPLORATORY SURVEY OF RESEARCHERS AND DECISION MAKERS
Bibliographic record
Abstract
INTRODUCTION: HTA agencies, especially in developing countries, are under resourced and unable to conduct the desired amount of assessments. Adapting HTA reports (HTAs) from other jurisdictions is an alternative for saving resources. OBJECTIVES: To explore HTA transferability experiences in Latin-America and Caribbean (LAC): are decision makers (DMs) using HTAs from other jurisdictions? Are researchers adapting HTAs when developing local reports? How useful is the information found in HTAs from other jurisdictions? METHODS: Web-based survey sent to 13031 HTA researchers and DMs. RESULTS: We received 671 responses from 19 countries. DMs reported using HTAs from other jurisdictions to guide decisions in the majority of the situations: 52.6 percent HTAs from outside LAC (e.g., Europe), 23.1 percent from other LAC countries, and only 24.3 percent HTAs from their own countries. 63 percent of researchers reported using HTAs from other jurisdictions. Usefulness scored significantly higher for HTAs from other jurisdictions as compared to local HTAs (7.1 versus 6.0 in a 1-10 scale; p < .01). Both DMs and researchers considered the information regarding safety and effectiveness more applicable than the information on social aspects, or economic evaluation. Barriers that limit transferability had significantly different scores for HTAs from other LAC countries as compared to those from regions outside LAC (i.e., poor methodological quality 6.7 versus 5.3, different epidemiological context 6.0 versus 7.4; all p < .01). CONCLUSIONS: HTAs from outside the region are commonly used. However, DMs and researchers agreed that HTAs from LAC had the greatest potential for transferability, provided that barriers such as poor methodological quality could be overcome.
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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 | 0.071 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".