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Record W4410967136 · doi:10.1080/15381501.2025.2505845

A scoping review of Indigenous Peoples’ conceptualization of HIV pre-exposure prophylaxis in Australia, Canada, New Zealand and the United States and its applications to Canada

2025· review· en· W4410967136 on OpenAlexafffundabout
Timothy Lim, Jamie Koscak, Jaris Swidrovich

Bibliographic record

VenueJournal of HIV & Social Services · 2025
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsConceptualizationIndigenousHuman immunodeficiency virus (HIV)Pre-exposure prophylaxisPolitical scienceGeographyEnvironmental planningMedicineFamily medicineEcologyMen who have sex with menBiologyComputer science

Abstract

fetched live from OpenAlex

Despite increasing PrEP use in Canada, uptake remains low among Indigenous Peoples, who experience disproportionately higher rates of HIV. A scoping review following Arksey and O’Malley’s framework and PRISMA-ScR was conducted using literature from 2012 to 2024 to explore how Indigenous Peoples conceptualize PrEP regarding awareness, motivation, access, and effectiveness. We included qualitative, quantitative, and mixed-method studies and reports from Canada, Australia, New Zealand, and the United States. Out of 181 identified articles, 11 met our inclusion criteria. The studies highlighted a lack of Indigenous People identifying with mainstream gay communities where PrEP promotion usually occurs. Individuals were motivated to use PrEP to prevent getting HIV and if PrEP was funded. Healthcare provider barriers such as overburdened Indigenous-specific services and a focus on chronic diseases, along with HIV-related stigma and homophobia, limit access to PrEP. This review also revealed a lack of effective use of PrEP by Indigenous populations.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.228
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.367
Teacher spread0.343 · 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 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
Published2025
Admission routes3
Has abstractyes

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