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Record W3160346390 · doi:10.2196/27168

Web-Based Multifaceted Approach for Community-Based HIV Self-Testing Among Female Sex Workers in Indonesia: Protocol for a Randomized Community Trial

2021· article· en· W3160346390 on OpenAlexvenueno aff
Jessie Olivia Yunus, Anak Agung Sagung Sawitri, Dewa Nyoman Wirawan, I Gusti Agung Agus Mahendra, Dewi Susanti, Ni Kadek Ayu Dwi Utami Ds, Dedison Asanab, Ida Ayu Narayani, Oldri Sherli Mukuan, Asti Widihastuti, Robert J. Magnani, Pande Putu Januraga

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsSex workersProtocol (science)Human immunodeficiency virus (HIV)Men who have sex with menRandomized controlled trialSex workMedicineModalitiesWeb applicationFamily medicinePopulationWorld Wide WebEnvironmental healthAlternative medicineComputer scienceResearch methodologySurgerySociologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: New HIV infections in Indonesia continue to be concentrated among key populations, including female sex workers (FSWs). However, increasing HIV testing among this subpopulation remains a challenge, necessitating exploration into alternative testing modalities. OBJECTIVE: This study aims to assess whether the addition of an oral fluid testing option in community settings would increase the rate of HIV case identification among FSWs. Because the study was implemented early in the outbreak of COVID-19 in Indonesia, a secondary objective is to assess approaches and tools for implementing both community outreach and community HIV screening for FSWs during pandemic conditions. METHODS: We undertook a community-based randomized trial in 23 national priority districts in which community outreach services were being provided. Community-based screening using an oral fluid-based rapid test was added to the community outreach standard of care in intervention districts with clients having the option of performing the test themselves or being assisted by outreach workers. A web-based system was created to screen for eligibility and collect participant data and test results, facilitating the process for both unassisted and assisted participants. Participants with reactive screening results were encouraged to undergo HIV testing at a health facility to confirm their diagnosis and initiate antiretroviral treatment as needed. Multiple means of recruitment were deployed including through outreach workers and social media campaigns. RESULTS: Of the 1907 FSWs who registered, met the eligibility criteria, and gave consent to participate, 1545 undertook community oral fluid test (OFT) screening. Most (1516/1545, 98.1%) opted for assisted screening. Recruitment via social media fell far short of expectations as many who registered independently for the OFT because of the social media campaign did not identify as FSWs. They were eventually not eligible to participate, but their interest points to the possibility of implementing HIV self-testing in the general population. The successful recruitment through outreach workers, facilitated by social media, indicates that their roles remain crucial in accessing FSW networks and improving HIV testing uptake. CONCLUSIONS: The addition of HIV self-testing to the standard of care supported by a web-based data collection system was able to increase HIV case identification among FSWs in intervention districts. The high satisfaction of OFT users and the interest of the general population toward this alternative testing modality are promising for scaling up community HIV screening nationally. TRIAL REGISTRATION: ClinicalTrials.gov NCT04578145; https://clinicaltrials.gov/ct2/show/NCT04578145. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/27168.

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)0.050
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.085
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.040
Meta-epidemiology (narrow)0.0070.004
Meta-epidemiology (broad)0.0110.006
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0050.005
Open science0.0040.003
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0850.016

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 head0.325
GPT teacher head0.539
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations2
Published2021
Admission routes1
Has abstractyes

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