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Record W2765469439 · doi:10.1504/ijhtm.2017.10008509

Perceptions of health technology assessment knowledge translation in China: a qualitative study on HTA researchers and policy-makers

2017· article· en· W2765469439 on OpenAlexaff
Yan Wei, Raymond Pong, Lizheng Shi, Jian Ming, Meng Tang, Yiwei Mao, Wenbin Liu, Yingyao Chen

Bibliographic record

VenueInternational Journal of Healthcare Technology and Management · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsHealth technologyKnowledge translationChinaQualitative researchHealth policyKnowledge managementPolitical sciencePublic relationsBusinessSociologyHealth careComputer scienceSocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.482
GPT teacher head0.606
Teacher spread0.124 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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