Charting the way forward for HTA in Asia-Pacific: HTAsiaLink’s strategic plan
Bibliographic record
Abstract
Health Technology Assessment (HTA) informs resource allocation and policy decisions, particularly to achieve Universal Health Coverage (UHC). Recognizing the increasing demand for evidence-informed decision-making, the HTAsiaLink network was established in 2011 as a regional platform to strengthen individual and institutional capacity in HTA research and facilitate the integration of HTA evidence into policy decisions across the Asia-Pacific.Over the years, HTAsiaLink has expanded to over fifty members from twenty economies. In 2024, a structured strategic planning process was undertaken to ensure its continued growth and strengthen its impact on HTA development and implementation. This process involved a targeted review of strategic plans from international networks, alongside comprehensive member engagement, to develop a data-driven and adaptable plan responsive to the evolving healthcare landscape and member needs. As a result, five strategic priorities, corresponding action items, and success indicators were identified.This commentary outlines the needs and processes involved in developing the network's first-ever strategic plan, emphasizing the critical role of member engagement in shaping its future direction. We believe that this experience offers transferable insights for other HTA networks, particularly those operating in low- and middle-income country contexts, on the collaborative development of strategic plans that are responsive to shared objectives, accommodate varying institutional capacities, and align with regional priorities.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".