MAPPING OF HEALTH TECHNOLOGY ASSESSMENT IN SELECTED COUNTRIES
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to develop and apply an instrument to map the level of health technology assessment (HTA) development at country level in selected countries. We examined middle-income countries (Argentina, Brazil, India, Indonesia, Malaysia, Mexico, and Russia) and countries well-known for their comprehensive HTA programs (Australia, Canada, and United Kingdom). METHODS: A review of relevant key documents regarding the HTA process was performed to develop the instrument which was then reviewed by selected HTAi members and revised. We identified and collected relevant information to map the level of HTA in the selected countries. This was supplemented by information from a structured survey among HTA experts in the selected countries (response rate: 65/385). RESULTS: Mapping of HTA in a country can be done by focusing on the level of institutionalization and the HTA process (identification, priority setting, assessment, appraisal, reporting, dissemination, and implementation in policy and practice). Although HTA is most advanced in industrialized countries, there is a growing community in middle-income countries that uses HTA. For example, Brazil is rapidly developing effective HTA programs. India and Russia are at the very beginning of introducing HTA. The other middle-income countries show intermediate levels of HTA development compared with the reference countries. CONCLUSIONS: This study presents a set of indicators for documenting the current level and trends in HTA at country level. The findings can be used as a baseline measurement for future monitoring and evaluation. This will allow a variety of stakeholders to assess the development of HTA in their country, help inform strategies, and justify expenditure for HTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".