Feasibility, Acceptability, Satisfaction, and Challenges of an mHealth App (e-ASCov) for Community-Based COVID-19 Screening by Community Health Workers in Rwanda: Mixed Methods Study
Bibliographic record
Abstract
Background: Although at the base of the pyramid-shaped organization of the Rwandan health system, community health workers (CHWs) are central to the community-based management of disease outbreaks. Objective: This mixed methods study aimed to explore the feasibility, acceptability, satisfaction, and challenges of a mobile health (mHealth) tool for community-based COVID-19 screening in Rwanda. Methods: Two urban (Gasabo and Nyarugenge) and 2 rural (Rusizi and Kirehe) districts in Rwanda participated in the project (smartphone app for COVID-19 screening). A mixed methods approach was used to inform the feasibility (awareness and expectation), acceptability (use and perceived benefits), satisfaction, and challenges of the mHealth intervention. At the end of the project, CHWs were asked to complete a quantitative questionnaire on the use of and satisfaction with the app. Then, in-depth interviews and focus group discussions were organized with CHWs. A content analysis was performed on the transcripts. Results: Overall, 383 CHWs were recruited and trained; 378 CHWs participated in the study. The mean age of CHWs was 36.7 (SD 6.6) to 45.3 (SD 9.9) years and most were women (237/378, 62.7%). More than 7000 people were registered with the use of the app and 20% were referred to a local COVID-19 testing facility. According to CHW reporting, the median number of people screened by each CHW ranged from 152 (IQR 70-276) for Nyarugenge to 24 (IQR 16-90) for Rusizi. COVID-19 positivity rates were higher in urban than rural districts: more than half of the CHWs in Gasabo reported a confirmed positive case versus only 2.4% for Kirehe and 15.4% for Rusizi. Despite the app being a novel tool, CHWs were well aware of the use of such a tool and had appropriate expectations. Acceptability and satisfaction were very high, with differences between urban and rural districts. Satisfaction was higher in Nyarugenge (72.8/100) and Gasabo (80.7/100) than in Kirehe (61.6/100) and Rusizi (64.5/100). More than 80% of the CHWs were willing to continue using the e-ASCov app, with the exception of CHWs in Kirehe (56.7%). The app was perceived as a tool to generate information on COVID-19, inform on the status of the pandemic, and help curb the spread of the pandemic in Rwanda. CHWs were satisfied with the app at all stages of its implementation in their districts. Conclusions: In this proof-of-concept study, a smartphone app for screening COVID-19 was useful as an mHealth tool to be used by CHWs, with the potential to increase health system efficiency in an epidemic context. The context should be analyzed for generalization on a country-wide scale, both in case of an epidemic and to take into account certain conditions at the community level. Information is needed on the conditions of generalization and transferability of this type of app to other health conditions so that CHWs can be given their full place in a pyramidal health system.
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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 | 0.018 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".