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Record W4238493279 · doi:10.32920/ryerson.14651988

Community Based HIV Prevention Interventions in Africa: A Systematic Review

2021· review· en· W4238493279 on OpenAlexaff
Sarah A. Ibrahim

Bibliographic record

Venuenot available
Typereview
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychological interventionUnderpinningIntervention (counseling)Human immunodeficiency virus (HIV)Coding (social sciences)PsychologyEnvironmental healthMedicineFamily medicineSociologyEngineeringNursingSocial science

Abstract

fetched live from OpenAlex

This systematic review is the first to examine the characteristics of community-based HIV prevention interventions, specifically the theoretical underpinning, type and number of components, dose, mode of delivery, and teaching method; and explore the effects of the intervention characteristics on HIV-related knowledge and engagement in risk behaviour among young persons in Africa. A total of 5 studies were included in this review. Conceptual and operational definitions of the intervention characteristics guided the coding and extraction of data from the reports. The overall results of this study showed variability in theoretical underpinning, dose, and mode of delivery of interventions. Multi-component interventions using mixed teaching method produced the desired effect on knowledge and risk behaviour. The examination of community based HIV prevention intervention characteristics provides direction for the development of efficient future interventions to decrease the transmission of HIV among young persons 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0110.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.588
GPT teacher head0.578
Teacher spread0.010 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes1
Has abstractyes

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