MétaCan
Menu
Back to cohort
Record W2947921208 · doi:10.1080/09540121.2019.1622642

HIV testing among men in Nigeria: a comparative analysis between young people and adults

2019· article· en· W2947921208 on OpenAlexaff
Babayemi O. Olakunde, Daniel A Adeyinka, John Olajide Olawepo, Jennifer R. Pharr

Bibliographic record

VenueAIDS Care · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDemographyMedicineLogistic regressionCondomHuman immunodeficiency virus (HIV)Young adultSerostatusSexually activeAnal intercourseOddsSexual intercourseGerontologyMen who have sex with menPopulationEnvironmental healthViral loadImmunology

Abstract

fetched live from OpenAlex

HIV testing among men is critical to ending the HIV epidemic in sub-Saharan Africa. Using the Multiple Indicator Cluster Survey, 2016/2017, we examined the uptake and determinants of HIV testing among sexually active men in Nigeria. A total of 1254 young people (15–24 years) and 7866 adults (25–49 years) were included in the analysis. We conducted binary logistic regression analyses to estimate the odds ratio (OR) and adjusted OR for testing for HIV in the last 12 months preceding the survey. Approximately 18.7% of men had tested for HIV (young people [17%] vs. adult [19%], p=0.125). The overall adjusted model showed that the likelihood of HIV testing was significantly higher among those with at least primary education, currently married, who used condom at last sexual intercourse, who drank alcohol one month preceding the survey, with no discriminatory attitudes towards people living with HIV (PLHIV), exposed to media, in the rich and richest quintiles, and in the North Central Zone. Education, geopolitical zone, and discriminatory attitudes towards PLHIV were the significant factors common to both age groups. Our results suggest that HIV testing among sexually active men in Nigeria is low, and the determinants vary between young people and adults.

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 score0.033
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.321
Teacher spread0.301 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAIDS CareSame topicHIV/AIDS Research and InterventionsFrench-language works237,207