The newcomer’s guide to health technology assessment: a collection of resources for early career professionals
Bibliographic record
Abstract
Capacity building for health technology assessment (HTA) holds a pivotal position in shaping the landscape of healthcare decision-making. It is one of the key strategic goals of Health Technology Assessment International (HTAi) for the development and use of HTA around the globe. Over the past decade, HTAi has initiated several activities to define capacity building for HTA. This endeavor underscores the multifaceted nature of capacity building within HTA, encompassing the development or enhancement of competencies crucial for understanding, contributing to, executing, or leveraging HTA for health policy formulation and decision-making. Moreover, capacity building extends to fostering awareness and gathering support within the broader ecosystem where HTA operates.
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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 | 0.038 | 0.171 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.140 | 0.169 |
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".