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Record W4415386316 · doi:10.2196/64816

Acceptability, Needs, Concerns, and Barriers to Digital-Based Interventions for the Prevention of Mother-to-Child Transmission of HIV: Systematic Review and Qualitative Meta-Aggregation

2025· article· en· W4415386316 on OpenAlexvenueno aff
Sidik Maulana, Kusman Ibrahim, Rachel Arbing, Iqbal Pramukti, Annisa Dewi Nugrahani, Luh Nik Armini, Muhammad Iqhrammullah, Wei‐Ti Chen

Bibliographic record

VenueJMIR Medical Informatics · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsConfidentialityPsychological interventionQualitative researchIntervention (counseling)TelehealthDigital healthMEDLINEQualitative property

Abstract

fetched live from OpenAlex

Background: Digital-based interventions have the potential to support initiatives for the prevention of mother-to-child HIV transmission (PMTCT). Nevertheless, reviews to explore experiences and perspectives toward digital-based interventions in mothers living with HIV remain limited. Objective: The aim of the study was to explore the experiences and perspectives toward digital-based interventions in promoting PMTCT services in mothers living with HIV. Methods: This study conducted a systematic review and qualitative meta-aggregation and adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and the Joanna Briggs Institute (JBI) Reviewer's Manual. Electronic databases such as Scopus, PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Wiley Online Library were systematically searched on July 5, 2024. The eligibility criteria included qualitative studies that focus on mothers living with HIV and health care providers, exploring their experiences and attitudes toward digital-based interventions for the PMTCT of HIV. The quality of the studies was assessed using the JBI Critical Appraisal tools for qualitative research and the Mixed Methods Appraisal Tool (MMAT) for mixed methods studies. The meta-aggregation was used to synthesize findings from included qualitative studies. Results: The 8 included studies (3 qualitative and 5 mixed-methods studies) were conducted in Kenya, South Africa, and India and evaluated mobile-based interventions such as SMS, phone calls, and mobile apps. The findings were synthesized into overarching themes: (1) positive acceptability of digital-based intervention for PMTCT services; (2) the need for integrating education, support systems, and reminders into digital-based intervention among mothers living with HIV; (3) concerns about confidentiality; and (4) personal, interpersonal, and health care-related barriers to care adherence. These themes were divided into 9 categories, including perceived satisfaction, improved adherence, the need for education, support systems, reminders, concerns about their privacy, lack of family support, financial constraints, and negative provider attitudes. Conclusions: Although most included studies were limited, their findings highlight the insight that the integration of digital-based interventions is perceived as acceptable and beneficial in strengthening PMTCT services delivery among mothers living with HIV. Mobile-based tools were valued for delivering education and reminders and facilitating communication with providers. However, concerns about confidentiality and persistent structural barriers must be addressed. To strengthen PMTCT services, it is essential to integrate user-centered digital tools into maternal care, supported by policies that ensure data privacy and equitable access.

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)0.086
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.086
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0860.180
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0140.026
Bibliometrics0.0140.014
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.439
Teacher spread0.384 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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