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Record W2599020891 · doi:10.7448/ias.20.1.21371

Improving antiretroviral therapy adherence in resource‐limited settings at scale: a discussion of interventions and recommendations

2017· article· en· W2599020891 on OpenAlexaff
Jessica E. Haberer, Lora Sabin, K. Rivet Amico, Catherine Orrell, Omar Galárraga, Alexander C. Tsai, Rachel Vreeman, Ira B. Wilson, Nadia A. Sam‐Agudu, Terrence F. Blaschke, Bernard Vrijens, Claude A. Mellins, Robert H. Remien, Sheri D. Weiser, Elizabeth D. Lowenthal, Michael J. Stirratt, Papa Salif Sow, Bruce Thomas, Nathan Ford, Edward J. Mills, Richard Lester, Jean B. Nachega, Bosco M. Bwana, Fred M. Ssewamala, Lawrence Mbuagbaw, Paula Munderi, Elvin Geng, David R. Bangsberg

Bibliographic record

VenueJournal of the International AIDS Society · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcMaster UniversityImpactUniversity of British ColumbiaUniversity of Ottawa
FundersNational Institute of Mental HealthWorld Health OrganizationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBill and Melinda Gates Foundation
KeywordsPsychological interventionMedicineSystematic reviewIncentiveHealth carePopulationScale (ratio)MEDLINENursingEnvironmental healthEconomic growthPolitical science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.072
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.072
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.110
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0070.006
Science and technology studies0.0030.004
Scholarly communication0.0090.012
Open science0.0070.005
Research integrity0.0110.014
Insufficient payload (model declined to judge)0.0070.002

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 head0.036
GPT teacher head0.364
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations196
Published2017
Admission routes1
Has abstractyes

Explore more

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