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Record W2995048391 · doi:10.2196/15281

The Utility of SMS to Report Male Partner HIV Self-testing Outcomes Among Women Seeking Reproductive Health Services in Kenya: Cohort Study

2019· article· en· W2995048391 on OpenAlexvenueno aff
Alison L. Drake, Emily R. Begnel, Jillian Pintye, John Kinuthia, Anjuli D. Wagner, C. Rothschild, Felix Otieno, Valarie Kemunto, Jared M. Baeten, Grace John‐Stewart

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthCenter for AIDS Research, University of WashingtonUniversity of WashingtonFred Hutchinson Cancer Research CenterU.S. Department of State
KeywordsMedicineFamily medicineCohortHuman immunodeficiency virus (HIV)Reproductive healthTest (biology)Hiv testShort Message ServicePopulationHealth facilityEnvironmental healthHealth services

Abstract

fetched live from OpenAlex

BACKGROUND: Use of SMS for data collection is expanding, but coverage, bias, and logistical constraints are poorly described. OBJECTIVE: The aim of this study is to assess the use of SMS to capture clinical outcomes that occur at home and identify potential biases in reporting compared to in-person ascertainment. METHODS: In the PrEP Implementation in Young Women and Adolescents program, which integrated pre-exposure prophylaxis (PrEP) into antenatal care, postnatal care, and family planning facilities in Kisumu County, Kenya, HIV-negative women 14 years of age or older were offered oral HIV self-tests (HIVSTs) to take home to male partners. Women that brought a phone with a Safaricom SIM to the clinic were offered registration in an automated SMS system (mSurvey) to collect information on HIVST outcomes. Women were asked if they offered the test to their male partners, and asked about the test process and results. HIVST outcomes were collected via SMS (sent 2.5 weeks later), in-person (if women returned for a follow-up scheduled 1 month later), or using both methods (if women initiated PrEP, they also had scheduled follow-up visits). The SMS prompted women to reply at no charge. HIVST outcomes were compared between women with scheduled follow-up visits and those without (follow-up visits were only scheduled for women who initiated PrEP). HIVST outcomes were also compared between women reporting via SMS and in-person. RESULTS: Among 2123 women offered HIVSTs and mSurvey registration, 486 (23.89%) accepted HIVSTs, of whom 359 (73.87%) were eligible for mSurvey. Additionally, 76/170 (44.7%) women with scheduled follow-up visits and 146/189 (77.3%) without scheduled follow-up visits registered in mSurvey. Among the 76 women with scheduled follow-ups, 62 (82%) had HIVST outcomes collected: 19 (31%) in-person, 20 (32%) by SMS, and 23 (37%) using both methods. Among the 146 women without scheduled visits, 87 (59.6%) had HIVST outcomes collected: 3 (3%) in-person, 82 (94%) by SMS, and 2 (2%) using both methods. SMS increased the collection of HIVST outcomes substantially for women with scheduled follow-up visits (1.48-fold), and captured 82 additional reports from women without scheduled follow-up visits. Among 222 women with reported HIVST outcomes, frequencies of offering partners the HIVST (85/95, 89% in-person vs 96/102, 94% SMS; P=.31), partners using the HIVST (83/85, 98% vs 92/96, 96%; P=.50), women using HIVST with partners (82/83, 99% vs 91/92, 99%; P=.94), and seeing partner's HIVST results (82/83, 99% vs 89/92, 97%; P=.56) were similar between women reporting in-person only versus by SMS only. However, frequency of reports of experiencing harm or negative reactions from partners was more commonly reported in the SMS group (17/102, 16.7% vs 2/85, 2%; P=.003). Barriers to the SMS system registration included not having a Safaricom SIM or a functioning phone. CONCLUSIONS: Our results suggest that the use of SMS substantially improves completeness of outcome data, does not bias reporting of nonsensitive information, and may increase reporting of sensitive information. .

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.443
Teacher spread0.387 · 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 designObservational
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

Citations5
Published2019
Admission routes1
Has abstractyes

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