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
Bibliographic record
Abstract
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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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".