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Record W2979812636 · doi:10.1017/s0266462319000667

Landscape analysis of health technology assessment (HTA): systems and practices in Asia

2019· article· en· W2979812636 on OpenAlexaff
Yot Teerawattananon, Waranya Rattanavipapong, Lydia Lin, Saudamini Vishwanath Dabak, Brent Gibbons, Wanrudee Isaranuwatchai, Kai Yee Toh, Boon Piang Cher, Fiona Pearce, Diana Beatriz Bayani, Ryota Nakamura, Raoh‐Fang Pwu, Asrul Akmal Shafie, Deepika Adhikari, Shankar Prinja, Wendy Babidge

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsSt. Michael's Hospital
FundersJapan Society for the Promotion of ScienceRockefeller FoundationThailand Research FundOverseas Development InstituteDepartment for International DevelopmentBill and Melinda Gates Foundation
KeywordsHealth technologyProcess (computing)Relevance (law)Inclusion (mineral)BusinessProcess managementPolitical scienceManagement scienceKnowledge managementComputer scienceSociologyEngineeringHealth care

Abstract

fetched live from OpenAlex

This paper explores the characteristics of health technology assessment (HTA) systems and practices in Asia. Representatives from nine countries were surveyed to understand each step of the HTA pathway. The analysis finds that although there are similarities in the processes of HTA and its application to inform decision making, there is variation in the number of topics assessed and the stakeholders involved in each step of the process. There is limited availability of resources and technical capacity and countries adopt different means to overcome these challenges by accepting industry submissions or adapting findings from other regions. Inclusion of stakeholders in the process of selecting topics, generating evidence, and making funding recommendations is critical to ensure relevance of HTA to country priorities. Lessons from this analysis may be instructive to other countries implementing HTA processes and inform future research on the feasibility of implementing a harmonized HTA system in the region.

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)0.014
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0080.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.156
GPT teacher head0.515
Teacher spread0.359 · 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 designObservational
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

Citations52
Published2019
Admission routes1
Has abstractyes

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