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Record W2022815261 · doi:10.1080/09540120902729932

HIV and tuberculosis in Durban, South Africa: adherence to two medication regimens

2009· article· en· W2022815261 on OpenAlexaff
Inge B. Corless, Dean Wantland, Busi Bhengu, Patricia McInerney, Busisiwe P. Ncama, Patrice K. Nicholas, Chris A. McGibbon, Emily Wong, Sheila Davis

Bibliographic record

VenueAIDS Care · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTuberculosisHuman immunodeficiency virus (HIV)MedicineFamily medicineTb treatmentGeographyVirologyPathology

Abstract

fetched live from OpenAlex

Given that antiretroviral (ARV) medication adherence has been shown to be high in resource-limited countries, the question remains as to whether adherence will remain at that level as medications become more widely available. Comparing adherence to tuberculosis (TB) medications, which have been readily available, and ARV medications may help to indicate the likely future adherence to ARVs as access to these medications becomes more widespread. This study examined sense of coherence, social support, symptom status, quality of life, and adherence to medications in two samples of individuals being treated either for TB or human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) at clinics in Durban, South Africa. Findings revealed the distinctive socio-economic backgrounds of the two cohorts. Although there were significant differences with regard to the psychosocial variables, there were no significant differences by the two samples in adherence to medications as well as adherence to appointments. Given the self-selected nature of the participants in this study, namely those able to attend clinic, as well as those likely to be adherent to ARVs, there is every reason for caution in the interpretations of these findings. As access to ARV medications becomes more widely available in South Africa, the question remains as to whether such high adherence will be maintained given the constraints of access to food and other basic necessities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.329
Teacher spread0.304 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2009
Admission routes1
Has abstractyes

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