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Record W4399177002 · doi:10.2196/55068

Mobile Phone–Based Confidential Social Network Referrals for HIV Testing (CONSORT): Protocol for a Randomized Controlled Trial

2024· article· en· W4399177002 on OpenAlexvenueno aff
Jan Ostermann, Bernard Njau, Marco van Zwetselaar, Thespina J. Yamanis, Leah McClimans, Rose Mwangi, Melkiory Beti, Amy Hobbie, Salome‐Joëlle Gass, Tara Mtuy, Nathan M. Thielman

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersFogarty International Center
KeywordsConfidentialityRandomized controlled trialProtocol (science)Consolidated Standards of Reporting TrialsMobile phoneInternet privacyMedicineHuman immunodeficiency virus (HIV)Family medicineComputer sciencePsychologyApplied psychologyComputer securityAlternative medicineTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Critical to efforts to end the HIV epidemic is the identification of persons living with HIV who have yet to be diagnosed and engaged in care. Expanded HIV testing outreach efforts need to be both efficient and ambitious, targeting the social networks of persons living with HIV and those at above-average risk of undiagnosed HIV infection. The ubiquity of mobile phones across many high HIV prevalence settings has created opportunities to leverage mobile health (mHealth) technologies to engage social networks for HIV testing outreach, prevention, and treatment. OBJECTIVE: The purpose of this study is to evaluate the acceptability and efficacy of a novel mHealth intervention, "Confidential Social Network Referrals for HIV Testing (CONSORT)," to nudge at-risk individuals to test for HIV using SMS text messages. METHODS: We will conduct the CONSORT study in Moshi, Tanzania, the commercial center and administrative capital of the Kilimanjaro Region in northern Tanzania. After qualitative formative work and pilot testing, we will enroll 400 clients presenting for HIV counseling and testing and 200 persons living with HIV and receiving care at HIV care and treatment centers as "inviters" into a randomized controlled trial. Eligible participants will be aged 18 years or older and live, work, or regularly receive care in Moshi. We will randomize inviters into 1 of 2 study arms. All inviters will be asked to complete a survey of their HIV testing and risk behaviors and to think of social network contacts who would benefit from HIV testing. They will then be asked to whom they would prefer to extend an HIV testing invitation in the form of a physical invitation card. Arm 1 participants will also be given the opportunity to extend CONSORT invitations in the form of automated confidential SMS text messages to any of their social network contacts or "invitees." Arm 2 participants will be offered physical invitation cards alone. The primary outcome will be counselor-documented uptake of HIV testing by invitees within 30 days of inviter enrollment. Secondary outcomes will include the acceptability of CONSORT among inviters, the number of new HIV diagnoses, and the HIV risk of invitees who present for testing. RESULTS: Enrollment in the randomized controlled trial is expected to start in September 2024. The findings will be disseminated to stakeholders and published in peer-reviewed journals. CONCLUSIONS: If CONSORT is acceptable and effective for increasing the uptake of HIV testing, given the minimal costs of SMS text reminders and the potential for exponential but targeted growth using chain referrals, it may shift current practices for HIV testing programs in the area. TRIAL REGISTRATION: ClincalTrials.gov NCT05967208; https://clinicaltrials.gov/study/NCT05967208. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55068.

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)0.061
metaresearch head score (Gemma)0.061
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.143
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.061
Meta-epidemiology (narrow)0.0090.005
Meta-epidemiology (broad)0.0140.006
Bibliometrics0.0040.006
Science and technology studies0.0050.006
Scholarly communication0.0070.005
Open science0.0040.003
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.1430.020

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 head0.418
GPT teacher head0.661
Teacher spread0.244 · 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
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
Published2024
Admission routes1
Has abstractyes

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