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Record W4280632233 · doi:10.1089/aid.2021.0200

Short Communication: Awareness of HIV Self-Care Interventions Across Global Regions: Results from a Values and Preferences Survey

2022· article· en· W4280632233 on OpenAlexaff
Kalonde Malama, Carmen H. Logie, Manjulaa Narasimhan, Leopold Ouédraogo, Chilanga Asmani, H. Elamin, L. Leigh-Ann van de Merwe, Jonathan Hopkins, Elizabeth A. Bukusi

Bibliographic record

VenueAIDS Research and Human Retroviruses · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersUNICEFWorld Health Organization
KeywordsPsychological interventionReproductive healthHuman immunodeficiency virus (HIV)MedicineHealth careGerontologyFamily medicineLatin AmericansGlobal healthEnvironmental healthPublic healthNursingPopulationEconomic growthPolitical science

Abstract

fetched live from OpenAlex

The high burden of HIV in sub-Saharan Africa places significant demands on health care services. Interventions such as HIV self-testing, and pre- and post-exposure prophylaxis (PrEP and PEP) could empower individuals to determine their HIV status and prevent HIV acquisition. In 2018, the World Health Organization disseminated an online, anonymous, global values and preferences survey to adults 18 years of age and older. The survey aimed to inform guidance on awareness, use, and preferences around self-care interventions for sexual and reproductive health. We conducted a cross-sectional analysis using Pearson's chi-squared test to compare awareness of HIV self-testing, PrEP and PEP across five global regions. Our analysis included 814 participants from 110 countries. We noted that respondents from Africa reported higher awareness of HIV interventions than participants from Europe, Latin America and the Caribbean, North America, and Asia. Our finding highlights an opportunity to expand self-care interventions for HIV prevention and management in Africa.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.208
GPT teacher head0.476
Teacher spread0.268 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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