Determinants of knowledge translation from health technology assessment to policy-making in China: From the perspective of researchers
Bibliographic record
Abstract
BACKGROUND: For health technology assessment (HTA) to be more policy relevant and for health technology-related decision-making to be truly evidence-based, promoting knowledge translation (KT) is of vital importance. Although some research has focused on KT of HTA, there is a dearth of literature on KT determinants and the situation in developing countries and transitional societies remains largely unknown. OBJECTIVE: To investigate the determinants of HTA KT from research to health policy-making from the perspective of researchers in China. DESIGN: Cross-sectional study. METHODS: A structured questionnaire which focused on KT was distributed to HTA researchers in China. KT activity levels in various fields of HTA research were compared, using one-way ANOVA. Principal component analysis was performed to provide a basis to combine similar variables. To investigate the determinants of KT level, multiple linear regression analysis was performed. RESULTS: Based on a survey of 382 HTA researchers, it was found that HTA KT wasn't widespread in China. Furthermore, results showed that no significant differences existed between the various HTA research fields. Factors, such as attitudes of researchers toward HTA and evidence utilization, academic ranks and linkages between researchers and policy-makers, had significant impact on HTA KT (p-values<0.05). Additionally, collaboration between HTA researchers and policy-makers, policy-relevance of HTA research, practicality of HTA outcomes and making HTA reports easier to understand also contributed to predicting KT level. However, academic nature of HTA research was negatively associated with KT level. CONCLUSION: KT from HTA to policy-making was influenced by many factors. Of particular importance were collaborations between researchers and policy-makers, ensuring policy relevance of HTA and making HTA evidence easier to understand by potential users.
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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 | 0.040 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".