Bibliographic record
Abstract
OBJECTIVES: The aim of the 2022 Health Technology Assessment International (HTAi) Asia Policy Forum (APF) was to discuss experiences and challenges around health technology assessment (HTA) capacity building for both HTA agencies and companies in the Asia region and to identify possible solutions as part of a capacity building roadmap. METHODS: Discussions during the 2022 APF, informed by a pre-meeting survey of HTA agencies and industry attendees from the region, form the basis of this paper. RESULTS: HTA is an essential element of priority-setting in healthcare; however, the scarcity of skilled technical HTA practitioners is a rate-limiting step in the conduct of HTA. The lack of investment in HTA and the political will to mandate the use of HTA in decision-making may be due to a lack of understanding of the value of the HTA process, and how HTA is interpreted and used in the healthcare decision-making process. CONCLUSIONS: Increased demand for HTA is created when the value of HTA is recognized. HTA capacity-building challenges may be mitigated by educating stakeholders, particularly policymakers, on the value of, and the need to invest in, HTA as a transparent process to ensure equitable access to health care for all. Investigating a means of funding and implementing an HTA intern program between agencies, in partnership with industry, to facilitate a supportive environment to foster HTA skills and knowledge, build capacity or strengthen existing capacity should be a priority.
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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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".