SMS-based digital health intervention in Rwanda's home-based care program for remote management of COVID-19 cases and contacts: A qualitative study of sustainability and scalability
Notice bibliographique
Résumé
Background COVID-19 pandemic resulted in unprecedented global health challenges. Rwanda identified its first COVID-19 case on March 14, 2020 and subsequently introduced Home-Base Care (HBC) Program in August 2020 following community transmission of the virus and to alleviate logistical and financial strain on the healthcare system. Cases and contacts eligible for HBC were remotely supported by WelTel, an SMS-based mHealth intervention that was successfully implemented before for HIV epidemic in Rwanda. Enrolled cases and contacts were supported and monitored daily via their cell and/or mobile phones until they complete isolation/quarantine period. This study explored the rationale, perspectives, and experiences of key informants (KIs) during the implementation WelTel's mHealth tool for HBC in Rwanda. Methods Semi-structured one-on-one virtual interviews were conducted with KIs in this qualitative study. The KIs were classified into 2 major categories: (A) Senior staff including policymakers, directors, and senior managers; (B) Technical teams including case managers, and other staff supporting the implementation of WelTel (e.g., IT staff). Interviews were audio-recorded, transcribed, and analyzed in NVivo. Thematic analysis was conducted using a hybrid approach. A topic guide was developed using the Modified Consolidated Framework for Implementation Research and feedback from local stakeholders. Results 7 KIs were interviewed. Five themes emerged following thematic analysis including: SMS-Based mHealth for Home-Isolation; Facilitators for Intervention Adoption; Barriers for Intervention Adoption; Infection prevention and control for Home-Isolation; and SMS-Based mHealth for Future Pandemics and Epidemics. Based on interviews, strong political commitment and advanced digital infrastructure were major facilitators for adopting WelTel for HBC. A major barrier to adopting WelTel was identified as technical-based issues. This was followed by local communication culture. All participates agreed on the significance of using WelTel to improve access and adherence to infection prevention and control measures, understand transmission dynamics, and inform public health decision-making regarding HBC. Conclusions Rwanda successfully adopted WelTel for supporting and monitoring COVID-19 cases and contacts in home-isolation and the implementation was instrumental to the country's effort to manage the pandemic. Experiences and perspectives of cases and contacts enrolled into WelTel must be explored to understand the appropriateness and effectiveness of the intervention.
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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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,006 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».