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Record W4388732207 · doi:10.1080/17450128.2023.2280059

Examining HIV-stigma interventions among youth living in sub-Sahara Africa: a systematic review of the evidence

2023· review· en· W4388732207 on OpenAlexaff
Eusebius Small, Silviya Nikolova, Thabani Nyoni, Yuan Zhou, Moses Okumu, Kim Lipsey, Megan R. Westmore, LaTisha Thomas

Bibliographic record

VenueVulnerable Children and Youth Studies · 2023
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStigma (botany)Psychological interventionHuman immunodeficiency virus (HIV)Social stigmaInclusion (mineral)MedicineIntervention (counseling)PsychologyClinical psychologyPsychiatryGerontologyEnvironmental healthFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

HIV-related stigma is a major barrier to accessing prevention, care, and treatment services. Unaddressed HIV stigma weakens the innovative scientific discoveries and efforts of researchers, practitioners, and policymakers to combat HIV/AIDS. The review investigated the effects of stigma-informed intervention studies conducted in sub-Saharan Africa that measured an outcome related to HIV. We reviewed 248 articles; 13 met our inclusion criteria and were the focus of this study. The findings showed a wide variety of intervention types, from specific to general stigma outcomes. Study outcomes were categorized into HIV prevention and treatment, emotional and behavioral, and external and perceived outcomes. Some studies showed positive effects on stigma; however, research methodologies across studies varied considerably. We conclude that more rigorous research is needed to build evidence for effective stigma reduction and uncover the social and cultural conditions that make HIV stigma so perversive.

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)0.033
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 score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.244
GPT teacher head0.404
Teacher spread0.159 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueVulnerable Children and Youth StudiesSame topicHIV/AIDS Research and InterventionsFrench-language works237,207