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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».