Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 5 min ago
Peer-reviewedCC BY 4.0JMIR Research Protocols

A Machine Learning–Guided Lyric Analysis Peer Support Intervention (LAPSI) for Psychological Distress in South African Women: Protocol for an Exploratory Sequential Mixed Methods Study

  • Lucas Banda1,2
  • Mathildah Mokgatle1
  • Olanrewaju Oladimeji3,4
4 affiliations
  1. 1Department of Public Health Faculty of Healthcare Sciences Sefako Makgatho Health Sciences University Pretoria, Gauteng South Africa
  2. 2Department of Social Sciences, Demography and Population Unit Faculty of Law, Humanities and Social Sciences Walter Sisulu University Mthatha, Eastern Cape South Africa
  3. 3Department of Public Health Faculty of Healthcare Sciences Sefako Makgatho Health Sciences University Garankuwa, Gauteng South Africa
  4. 4SAMRC/SMU Public Health Interventions, Innovations and Implementation Research Unit Pretoria, Gauteng South Africa
Read full textDOICite

Abstract

Background: Psychological distress disproportionately affects women in South Africa and is shaped by complex structural, socioeconomic, and psychosocial determinants, particularly within the context of HIV. Despite policy commitments to integrate mental health services into existing health systems, current interventions are limited in their capacity to address these intersecting determinants and are frequently insufficiently targeted, contextually responsive, or inclusive. Data-driven strategies that identify subgroup-specific vulnerabilities may improve the design of contextually relevant and culturally grounded psychosocial interventions.

Objective: This study seeks to develop and implement a machine learning (ML)–informed lyric analysis peer support intervention (LAPSI) and to evaluate its preliminary impact, feasibility, and acceptability among South African women living with or at heightened risk of HIV.

Methods: An exploratory sequential mixed methods design is used across five phases: (1) a scoping review of mental health interventions in sub-Saharan Africa; (2) ML-based risk profiling using national survey data with Shapley additive explanations (SHAP)–based interpretability; (3) intervention development and implementation; (4) a single-arm pre-post quantitative assessment of psychological distress using the Kessler 10-Item Psychological Distress Scale (K10), without a comparison group; and (5) qualitative evaluation of feasibility, usability, and acceptability.

Results: Secondary data analyses were conducted between October 2024 and April 2025. LAPSI was implemented between May and August 2025 in 2 provinces. Outcome assessments were completed in August 2025 (Eastern Cape) and September 2025 (Gauteng), and qualitative interviews were conducted 5 to 7 days after the intervention. A total of 30 participants were recruited and completed the intervention. Quantitative and qualitative analyses were ongoing at the time of manuscript submission, with full findings anticipated to be reported in 2026 and 2027.

Conclusions: This protocol describes a potentially scalable framework for integrating explainable ML with culturally grounded, peer-delivered intervention design to address psychological distress among women in HIV-affected settings.

International Registered Report Identifier (IRRID): DERR1-10.2196/90426

Related

  1. Sequential patient-spouse dyadic psychological intervention for perioperative emotional distress and dyadic coping in patients with spinal metastases.
    Liping Xie, Yunli Wang, Zhixiao Wang +9
    Frontiers in public healthPeer-reviewedCC BYFull textOncologyEurope PMCCrossref
  2. Controlled deep breathing technique intervention effects on resting EEG, subjective psychological distress, and executive function performance in young adults
    Ohav Ivgi, Oded Meiron
    Frontiers in public healthPeer-reviewedCC BYFull textEpidemiology & public healthEurope PMC
  3. Multimodal art intervention for flourishing in lonely university students: psychological and exploratory neurophysiological outcomes
    Wen Zhang, Siyu Tian, Ruikai Yuan
    Frontiers in psychologyPeer-reviewedCC BYFull textEurope PMC
  4. A five-day Mindfulness Based Intervention to reduce burnout and psychological distress in UK Medical Students
    A. Cromie, M. Kaur, N. Powell +1
    medRxivPreprintCC BYFull textGeneral medicinemedRxiv
  5. Sports betting frequency and psychological distress among youth in Greater Accra, Ghana: An exploratory cross-sectional study
    Kingsley Eyram King-Kuadzi
    Journal of Interventional Epidemiology and Public HealthPeer-reviewedCC BYFull textPsychiatry & mental healthDOAJ

Cite

BibTeXRIS
BibTeX
@article{banda2026machine,
  title = {{A Machine Learning–Guided Lyric Analysis Peer Support Intervention (LAPSI) for Psychological Distress in South African Women: Protocol for an Exploratory Sequential Mixed Methods Study}},
  author = {Lucas Banda and Mathildah Mokgatle and Olanrewaju Oladimeji},
  journal = {JMIR Research Protocols},
  year = {2026},
  month = oct,
  doi = {10.2196/90426},
  url = {https://doi.org/10.2196/90426},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A Machine Learning–Guided Lyric Analysis Peer Support Intervention (LAPSI) for Psychological Distress in South African Women: Protocol for an Exploratory Sequential Mixed Methods Study
AU  - Lucas Banda
AU  - Mathildah Mokgatle
AU  - Olanrewaju Oladimeji
PY  - 2026
DA  - 2026/10/21
JO  - JMIR Research Protocols
DO  - 10.2196/90426
UR  - https://doi.org/10.2196/90426
AB  - Background: Psychological distress disproportionately affects women in South Africa and is shaped by complex structural, socioeconomic, and psychosocial determinants, particularly within the context of HIV. Despite policy commitments to integrate mental health services into existing health systems, current interventions are limited in their capacity to address these intersecting determinants and are frequently insufficiently targeted, contextually responsive, or inclusive. Data-driven strategies that identify subgroup-specific vulnerabilities may improve the design of contextually relevant and culturally grounded psychosocial interventions.  Objective: This study seeks to develop and implement a machine learning (ML)–informed lyric analysis peer support intervention (LAPSI) and to evaluate its preliminary impact, feasibility, and acceptability among South African women living with or at heightened risk of HIV.  Methods: An exploratory sequential mixed methods design is used across five phases: (1) a scoping review of mental health interventions in sub-Saharan Africa; (2) ML-based risk profiling using national survey data with Shapley additive explanations (SHAP)–based interpretability; (3) intervention development and implementation; (4) a single-arm pre-post quantitative assessment of psychological distress using the Kessler 10-Item Psychological Distress Scale (K10), without a comparison group; and (5) qualitative evaluation of feasibility, usability, and acceptability.  Results: Secondary data analyses were conducted between October 2024 and April 2025. LAPSI was implemented between May and August 2025 in 2 provinces. Outcome assessments were completed in August 2025 (Eastern Cape) and September 2025 (Gauteng), and qualitative interviews were conducted 5 to 7 days after the intervention. A total of 30 participants were recruited and completed the intervention. Quantitative and qualitative analyses were ongoing at the time of manuscript submission, with full findings anticipated to be reported in 2026 and 2027.  Conclusions: This protocol describes a potentially scalable framework for integrating explainable ML with culturally grounded, peer-delivered intervention design to address psychological distress among women in HIV-affected settings.  International Registered Report Identifier (IRRID): DERR1-10.2196/90426
SN  - 1929-0748
ER  -