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Record W2970406343 · doi:10.1177/0956462419862899

‘What will it take’: addressing alcohol use among people living with HIV in South Africa

2019· article· en· W2970406343 on OpenAlexaff
Katherine Sorsdahl, Neo K. Morojele, CD Parry, CT Kekwaletswe, Naledi B. Kitleli, Megan Malan, PA Shuper, Bronwyn Myers

Bibliographic record

VenueInternational Journal of STD & AIDS · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCentre for Addiction and Mental Health
FundersSouth African Medical Research Council
KeywordsMedicinePsychological interventionContext (archaeology)Intervention (counseling)Focus groupHuman immunodeficiency virus (HIV)Brief interventionAlcoholAntiretroviral therapyFamily medicinePsychiatryEnvironmental healthViral load

Abstract

fetched live from OpenAlex

Given that hazardous and harmful alcohol use has been identified as a significant barrier to adherence to antiretroviral therapy (ART) in South Africa, alcohol reduction interventions delivered within HIV treatment services are being investigated. Prior to designing and implementing an alcohol-focused screening and brief intervention (SBI), we explored patients’ perceptions of alcohol as a barrier to HIV treatment, the acceptability of providing SBIs for alcohol use within the context of HIV services and identifying potential barriers to patient uptake of this SBI. Four focus groups were conducted with 23 participants recruited from three HIV treatment sites in Tshwane, South Africa. Specific themes that emerged included: (1) barriers to ART adherence, (2) available services to address problematic alcohol use and (3) barriers and facilitators to delivering a brief intervention to address alcohol use within HIV care. Although all participants in the present study unanimously agreed that there was a great need for SBIs to address alcohol use among people living with HIV and AIDS, our study identified several areas that should be considered prior to implementing such a programme.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.335
Teacher spread0.292 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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