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Record W2780803844 · 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· W2780803844 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

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcMaster UniversityUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsPsychological interventionMedicineSystematic reviewIncentiveHealth careScale (ratio)PopulationMEDLINENursingPolitical scienceEnvironmental health

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)0.071
metaresearch head score (Gemma)0.109
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: Commentary · Consensus signal: none
Teacher disagreement score0.071
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.109
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0060.005
Science and technology studies0.0030.004
Scholarly communication0.0090.011
Open science0.0070.005
Research integrity0.0100.013
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 head0.050
GPT teacher head0.334
Teacher spread0.284 · 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
GenreCommentary

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

Citations0
Published2017
Admission routes1
Has abstractyes

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