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Record W4387728806 · doi:10.1002/jia2.26185

Findings from the Tushirikiane mobile health (mHealth) HIV self‐testing pragmatic trial with refugee adolescents and youth living in informal settlements in Kampala, Uganda

2023· article· en· W4387728806 on OpenAlexafffund
Carmen H. Logie, Moses Okumu, Isha Berry, Robert Hakiza, Stefan Baral, Daniel Kibuuka Musoke, Aidah Nakitende, Simon Mwima, Peter Kyambadde, Miranda G. Loutet, Shamilah Batte, Richard Lester, Stella Neema, Katie Newby, Lawrence Mbuagbaw

Bibliographic record

VenueJournal of the International AIDS Society · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsImpactSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaPublic Health OntarioUnited Nations University Institute for Water, Environment, and HealthMcMaster UniversityWomen's College HospitalUniversity of Toronto
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsMedicineInformal settlementsmHealthRefugeeHuman immunodeficiency virus (HIV)Family medicineEnvironmental healthPsychiatryEconomic growth

Abstract

fetched live from OpenAlex

INTRODUCTION: Urban refugee youth remain underserved by current HIV prevention strategies, including HIV self-testing (HIVST). Examining HIVST feasibility with refugees can inform tailored HIV testing strategies. We examined if HIVST and mobile health (mHealth) delivery approaches could increase HIV testing uptake and HIV status knowledge among refugee youth in Kampala, Uganda. METHODS: We conducted a three-arm pragmatic controlled trial across five informal settlements grouped into three sites in Kampala from 2020 to 2021 with peer-recruited refugee youth aged 16-24 years. The intervention was HIVST and HIVST + mHealth (HIVST with bidirectional SMS), compared with standard of care (SOC). Primary outcomes were self-reported HIV testing uptake and correct status knowledge verified by point-of-care testing. Some secondary outcomes included: depression, HIV-related stigma, and adolescent sexual and reproductive health (SRH) stigma at three time points (baseline [T0], 8 months [T1] and 12 months [T2]). We used generalized estimating equation regression models to estimate crude and adjusted odds ratios comparing arms over time, adjusting for age, gender and baseline imbalances. We assessed study pragmatism across PRECIS-2 dimensions. RESULTS: We enrolled 450 participants (50.7% cisgender men, 48.7% cisgender women, 0.7% transgender women; mean age: 20.0, standard deviation: 2.4) across three sites. Self-reported HIV testing uptake increased significantly from T0 to T1 in intervention arms: HIVST arm: (27.6% [n = 43] at T0 vs. 91.2% [n = 135] at T1; HIVST + mHealth: 30.9% [n = 47] at T0 vs. 94.2% [n = 113] at T1]) compared with SOC (35.5% [n = 50] at T0 vs. 24.8% [ = 27] at T1) and remained significantly higher than SOC at T2 (p<0.001). HIV status knowledge in intervention arms (HIVST arm: 100% [n = 121], HIVST + mHealth arm: 97.9% [n = 95]) was significantly higher than SOC (61.5% [n = 59]) at T2. There were modest changes in secondary outcomes in intervention arms, including decreased depression alongside increased HIV-related stigma and adolescent SRH stigma. The trial employed both pragmatic (eligibility criteria, setting, organization, outcome, analysis) and explanatory approaches (recruitment path, flexibility of delivery flexibility, adherence flexibility, follow-up). CONCLUSIONS: Offering HIVST is a promising approach to increase HIV testing uptake among urban refugee youth in Kampala. We share lessons learned to inform future youth-focused HIVST trials in urban humanitarian settings.

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)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
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 head0.029
GPT teacher head0.338
Teacher spread0.309 · 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 designRandomized trial
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

Citations25
Published2023
Admission routes2
Has abstractyes

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