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Record W2035299773 · doi:10.1080/09540121.2011.565026

Socio-demographic correlates of late treatment initiation in a cohort of patients starting antiretroviral treatment in Mali, West Africa

2011· article· en· W2035299773 on OpenAlexafffund
Catherine M. Pirkle, Vinh‐Kim Nguyen, S. Ag Aboubacrine, Mamadou Cissé, Marı́a Victoria Zunzunegui

Bibliographic record

VenueAIDS Care · 2011
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsCohortMarital statusMedicineObservational studyLogistic regressionCohort studyDemographyAntiretroviral treatmentAntiretroviral therapyHuman immunodeficiency virus (HIV)GerontologyFamily medicinePopulationEnvironmental healthInternal medicineViral loadSociology

Abstract

fetched live from OpenAlex

The objective of this study was to investigate factors correlated with late treatment initiation in a cohort of patients starting treatment in Mali, West Africa, while focusing on the role of sex/gender. This study consisted of a cross-sectional analysis of baseline data from a prospective, observational cohort of patients initiating antiretroviral treatment in Mali. Patient data were analyzed with a gender perspective to examine factors correlated with late treatment initiation, defined as having a CD4 count below 100 cells/µl. Aday and Andersen's conceptual framework of access to medical care was used to classify baseline participant characteristics associated with late treatment initiation. Logistic regression was used to evaluate the modifying effect of sex/gender. Results show that 39% of patients initiated treatment late; significantly more of these were men than women. Sex/gender, marital status, and education were associated with late treatment initiation. Unmarried men and uneducated women were significantly more likely to initiate treatment late. Programs need to target unmarried men while being cognizant that uneducated women are arriving late as well.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.387

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 head0.029
GPT teacher head0.291
Teacher spread0.262 · 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

Citations6
Published2011
Admission routes2
Has abstractyes

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