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Record W6942720731 · doi:10.14288/1.0368707

Antiretroviral therapy interruption among HIV positive people who use drugs in a setting with a community-wide HIV treatment-as-prevention initiative

2018· article· en· W6942720731 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAntiretroviral therapyHuman immunodeficiency virus (HIV)Psychological interventionAdverse effectTreatment as preventionTransmission (telecommunications)Antiretroviral treatmentQualitative research

Abstract

fetched live from OpenAlex

HIV Treatment as Prevention (TasP) initiatives promote antiretroviral therapy (ART) access and optimal adherence (≥95 %) to produce viral suppression among people living with HIV (PLHIV) and prevent the onward transmission of HIV. ART treatment interruptions are common among PLHIV who use drugs and undermine the effectiveness of TasP. Semi-structured interviews were conducted with 39 PLHIV who use drugs who had experienced treatment ART interruptions in a setting with a community-wide TasP initiative (Vancouver, Canada) to examine influences on these outcomes. While study participants attributed ART interruptions to “treatment fatigue,” our analysis revealed individual, social, and structural influences on these events, including: (1) prior adverse ART-related experiences among those with long-term treatment histories; (2) experiences of social isolation; and, (3) breakdowns in the continuity of HIV care following disruptive events (e.g., eviction, incarceration). Findings reconceptualise ‘treatment fatigue’ by focusing attention on its underlying mechanisms, while demonstrating the need for comprehensive structural reforms and targeted interventions to optimize TasP among drug-using PLHIV.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.364
Teacher spread0.317 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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