Perceptions of health technology assessment knowledge translation in China: a qualitative study on HTA researchers and policy-makers
Bibliographic record
Abstract
Although health technology assessment (HTA) has existed in China since the 1980s, the integration of HTA and policy-making is still in its infancy. Knowledge translation (KT) can play an important role in facilitating evidence-based policy-making. This study aims to describe the process of KT from researches to policy-makers and identifying the main determinants of the use of HTA evidence in policy-making in China. Researchers' and health policy-makers' perceptions of HTA KT were identified, using a grounded theory analysis approach. A theoretical framework consisting of four domains emerged to represent researchers' and policy-makers' perceptions of KT from HTA to policy making. Health policy-makers and researchers identified several determinants of KT, including HTA KT processes, alignment between research and decision-making and features of HTA research and health decisions, communication between policy-makers and HTA researchers, and support and resources for KT (organisational support, researchers' and health policy-makers' personal relationships and macro environment support).
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 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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".