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Record W4402476271 · doi:10.1371/journal.pgph.0003687

Association between mass media exposure and HIV testing uptake in Cameroon

2024· article· en· W4402476271 on OpenAlexaff
Roger Antabe, Yujiro Sano, Daniel Amoak

Bibliographic record

VenuePLOS Global Public Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsWestern UniversityNipissing UniversityThe Scarborough Hospital
Fundersnot available
KeywordsMass mediaMedicineSocioeconomic statusDemographyHuman immunodeficiency virus (HIV)PsychosocialEnvironmental healthPopulationImmunologyAdvertisingPsychiatry

Abstract

fetched live from OpenAlex

In sub-Saharan African countries, mass media is critical in disseminating health information, including the need for HIV testing. Yet, in Cameroon, there is a dearth of studies examining how exposure to mass media is effective in the uptake of HIV testing. Using the 2018 Cameroon Demographic and Health Survey, we examined the association between exposure to mass media and HIV testing among sexually active women (n = 12,619) and men (n = 5,607). Our findings revealed a generally low uptake of HIV testing although more women (78%) have ever tested for HIV compared to men (67%). Adjusting for demographic, socioeconomic, and psychosocial factors, we found for both women and men their exposure at least once a week to the Internet (aOR = 1.57, p<0.001 for women; aOR = 1.76, p<0.001 for men), print media (aOR = 1.59, p<0.05 for women; aOR = 2.04, p<0.001 for men), radio (aOR = 1.34, p<0.01 for women; aOR = 1.57, p<0.001 for men), and television (aOR = 1.74, p<0.001 for women; aOR = 1.94, p<0.001 for men) was significantly associated with a higher likelihood of testing for HIV compared to their counterparts with no exposure at all. Our findings underscore the importance of further integrating mass media in HIV messaging in Cameroon as the country aims to achieve UNAIDS target 95-95-95 by 2023.

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)0.004
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 score0.140
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.207
GPT teacher head0.420
Teacher spread0.213 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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