DIFFERENCES IN EVALUATING HEALTH TECHNOLOGY ASSESSMENT KNOWLEDGE TRANSLATION BY RESEARCHERS AND POLICY MAKERS IN CHINA
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the gaps between researchers and policy makers in perceptions and influencing factors of knowledge translation (KT) of health technology assessment (HTA) in China. METHODS: A sample of 382 HTA researchers and 112 policy makers in China were surveyed using structured questionnaires. The questionnaires contained two sections: perceptions of HTA research and assessments of six-stage KT activities. Wilcoxon rank sum test was applied to compare the differences in these two sections between HTA researchers and policy makers. Multivariate linear regression was performed to explore KT determinants of HTA for researchers and policy makers separately. RESULTS: Policy makers and researchers differed in their perceptions of HTA research in all items except collaboration in research development and presentation of evidence in easy-to-understand language. Significant differences in KT activities existed in all the six stages except academic translation. Regarding KT determinants, close contact between research unit and policy-making department, relevance of HTA to policy making, and importance of HTA on policy making were considered facilitators by both groups. For researchers, practicality of HTA report and presentation of evidence in easy-to-understand language can facilitate KT. Policy makers, on the other hand, considered an overly pedantic nature of HTA research as an obstacle to effective KT. CONCLUSIONS: Substantial gaps existed between HTA researchers and policy makers regarding the perceptions of HTA research and KT activities. There are also some differences in KT determinants by these two groups. Enhancing collaboration, promoting practicality and policy relevance of HTA research, and making HTA findings easily understood are likely to further the KT of HTA evidence.
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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 | 0.086 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".