Digital Intervention to Improve Health Services for Young People in Zimbabwe: Process Evaluation of ‘Zvatinoda!’ (What We Want) Using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) Framework
Bibliographic record
Abstract
BACKGROUND: Youth in Southern Africa face a high burden of HIV and sexually transmitted infections, yet they exhibit low uptake of health care services. OBJECTIVE: The Zvatinoda! intervention, co-designed with youth, aims to increase the demand for and utilization of health services among 18-24-year-olds in Chitungwiza, Zimbabwe. METHODS: The intervention utilized mobile phone-based discussion groups, complemented by "ask the expert" sessions. Peer facilitators, supported by an "Auntie," led youth in anonymous online chats on health topics prioritized by the participants. Feedback on youth needs was compiled and shared with health care providers. The intervention was tested in a 12-week feasibility study involving 4 groups of 7 youth each, totaling 28 participants (n=14, 50%, female participants), to evaluate feasibility and acceptability. Mixed methods process evaluation data included pre- and postintervention questionnaires (n=28), in-depth interviews with participants (n=15) and peer facilitators (n=4), content from discussion group chats and expert guest sessions (n=24), facilitators' debrief meetings (n=12), and a log of technical challenges. Descriptive quantitative analysis and thematic qualitative analysis were conducted. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework was adapted to analyze and present findings on (1) reach, (2) potential efficacy, (3) adoption, (4) implementation, and (5) maintenance. RESULTS: Mobile delivery facilitated engagement with diverse groups, even during COVID-19 lockdowns (reach). Health knowledge scores improved from pre- to postintervention across 9 measures. Preintervention scores varied from 14% (4/28) for contraception to 86% (24/28) for HIV knowledge. After the intervention, all knowledge scores reached 100% (28/28). Improvements were observed across 10 sexual and reproductive health (SRH) self-efficacy measures. The most notable changes were in the ability to start a conversation about SRH with older adults in the family, which increased from 50% (14/28) preintervention to 86% (24/28) postintervention. Similarly, the ability to use SRH services even if a partner does not agree rose from 57% (16/28) preintervention to 89% (25/28) postintervention. Self-reported attendance at a health center in the past 3 months improved from 32% (9/28) preintervention to 86% (24/28) postintervention (potential efficacy). Chat participation varied, largely due to network challenges and school/work commitments. The key factors facilitating peer learning were interaction with other youth, the support of an older, knowledgeable "Auntie," and the anonymity of the platform. As a result of COVID-19 restrictions, regular feedback to providers was not feasible. Instead, youth conveyed their needs to stakeholders through summaries of key themes from chat groups and a music video presented at a final in-person workshop (adoption and implementation). Participation in discussions decreased over time. To maintain engagement, introducing an in-person element was suggested (maintenance). CONCLUSIONS: The Zvatinoda! intervention proved both acceptable and feasible, showing promise for enhancing young people's knowledge and health-seeking behavior. Potential improvements include introducing in-person discussions once the virtual group has established rapport and enhancing feedback and dialog with service providers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".