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When does evidence from clinical trials influence health policy? A qualitative study of officials in nine African countries of the factors behind the HIV policy decision to adopt Option B+

2018· article· en· W2898880723 on OpenAlexaff
Gordon DuVal, Seema Shah

Bibliographic record

VenueEvidence & Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCarleton University
Fundersnot available
KeywordsScientific evidenceHuman immunodeficiency virus (HIV)Clinical trialRandomized controlled trialMedicineQualitative researchEconomic growthPublic relationsPolitical scienceFamily medicineEconomicsSociologySurgery

Abstract

fetched live from OpenAlex

Introduction The appropriate role of evidence in health policy decision making is controversial and requires more data on how decisions are actually made. Option B+ is a strategy to prevent mother to child transmission (PMTCT) of HIV that involves starting pregnant, HIV-positive women on triple drug antiretroviral therapy (ART) and continuing for life. It was rapidly adopted by sub-Saharan African countries with limited scientific evidence for its efficacy and safety, without waiting for the results from an ongoing randomised controlled trial (RCT) comparing PMTCT strategies. Methods We interviewed 14 senior HIV policymakers in nine sub-Saharan African countries about factors influencing their adoption of Option B+. Results While scientific evidence was important to the decision to adopt Option B+, policymakers were persuaded by data that did not come from RCTs. Other factors also played an important role including: evidence for ancillary benefits, simplicity, alignment with other values and priorities, and ease of integration with existing programmes. Conclusions In adopting Option B+, gold-standard scientific evidence played a relatively minor role; other considerations were more important. Future research could help researchers determine whether these factors are influential in other contexts and to develop evidence that is more responsive to the needs of policymakers.

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)0.213
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.213
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.264
GPT teacher head0.601
Teacher spread0.337 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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