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
Notice bibliographique
Résumé
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.
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 | 0,018 | 0,014 |
| 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 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».