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Record W2088163790 · doi:10.1258/ijsa.2009.009355

A cross-sectional study of risk factors for HIV among pregnant women in Guatemala City, Guatemala: lessons for prevention

2010· article· en· W2088163790 on OpenAlexafffund
Mira Johri, Rosa E. Morales, Jeffrey S. Hoch, Blanca Samayoa, Cécile Sommen, Carlos Grazioso, J-F Boivin, Ingrid J Barrios Matta, Eva L Baide Diaz, Eduardo Arathoon

Bibliographic record

VenueInternational Journal of STD & AIDS · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill UniversityUniversity of TorontoUniversité de Montréal
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMedicinePsychological interventionPublic healthDemographyLogistic regressionCross-sectional studyEnvironmental healthGerontologyPsychiatryNursing

Abstract

fetched live from OpenAlex

Although the Central American HIV epidemic is concentrated in high-risk groups, HIV incidence is increasing in young women. From 2005 to 2007, we conducted a cross-sectional study of pregnant women in a large public hospital and an HIV clinic in Guatemala City to describe risk factors for HIV infection and inform prevention strategies. For 4629 consenting patients, HIV status was laboratory-confirmed and participant characteristics were assessed by interviewer-administered questionnaires. Lifetime number of sexual partners ranged from 1 to 99, with a median (interquartile range) of 1 (1, 2). 2.6% (120) reported exchanging sex for benefits; 0.1% (3) were sex workers, 2.3% (106) had used illegal drugs, 31.1% (1421) planned their pregnancy and 31.8% (1455) experienced abuse. In logistic regression analyses, HIV status was predicted by one variable describing women's behaviour (lifetime sexual partners) and three variables describing partner risks (partner HIV+, migrant worker or suspected unfaithful). Women in our sample exhibited few behavioural risks for HIV but significant vulnerability via partner behaviours. To stem feminization of the epidemic, health authorities should complement existing prevention interventions in high-risk populations with directed efforts towards bridging populations such as migrant workers. We identify four locally adapted HIV prevention strategies.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.420
Teacher spread0.369 · 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

Citations10
Published2010
Admission routes2
Has abstractyes

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