Fifteen Lessons from Fifteen Years of the Health Intervention and Technology Assessment Program in Thailand
Bibliographic record
Abstract
The Health Intervention and Technology Assessment Program (HITAP) was established in 2007. This article highlights 15 lessons from over 15 years of experience, noting five achievements about what HITAP has done well, five areas that it is currently working on, and five aims for work in the future. HITAP built capacity for HTA and linked research to policy and practice in Thailand. With collaborators from academic and policy spheres, HITAP has mobilized regional and global support, and developed global public goods to enhance the field of HTA. HITAP's semi-autonomous structure has facilitated these changes, though they have not been without their challenges. HITAP aims to continue its work on HTA for public health interventions and disinvestments, effectively engaging with stakeholders and strategically managing its human resources. Moving forward, HITAP will develop and update global public goods on HTA, work on emerging topics such as early HTA, address issues in digital health, real-world evidence and equity, support HTA development globally, particularly in low-income settings, and seek to engage more effectively with the public. HITAP seeks to learn from its experience and invest in the areas identified so that it can grow sustainably. Its journey may be relevant to other countries and institutions that are interested in developing HTA programs.
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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 | 0.053 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 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".