End User Experience of Hayat App and Dashboard: A Qualitative Assessment of Usability, Community Engagement, and Validity of Data for Antenatal Care Provision And Routine Immunization in Rural Pakistan And Afghanistan
Notice bibliographique
Résumé
Abstract Introduction Pakistan and Afghanistan have an extensive network of community health workers (CHWs) who provide counseling to rural households on basic maternal and child care (MCH), report household service coverage, and provide referrals to health centers. An android-based mobile health application for maternal and child health was piloted in underserved remote areas within Northern Pakistan and bordering Bamyan and Badakshan provinces of Afghanistan to enable community health workers in Afghanistan and both community health workers and vaccinators in Pakistan, to report real-time data on outreach visits as well as immunization and maternity health coverage of eligible clients. A qualitative assessment of health worker experience with the Mobile App was carried out as part of the end-line assessment of the pilot. Objective The objective was to examine the end-user perceptions of the usability of the digital application data, community acceptability of the data, and use of data supervision and management decisions. The purpose was to identify barriers and enablers to inform the integration of the mhealth application for reporting by community health workers within the district health systems in an LMIC setting. Methods Primary data was collected through focus group discussions with frontline health workers and key informant interviews with field supervisors as well as sub-national managers. Seventeen focus group discussions were carried out within purposely selected study catchment sites. These included 9 FGDs with community-based Lady Health Workers (LHWs), LHW supervisors, and vaccinators in Northern Pakistan; and 8 FGDs with Community Health Workers (CHWs) and CHW supervisors. Additionally, 28 key informant interviews were carried out with field supervisors, immunization, and MCH managers at the district and provincial levels. Deductive thematic content analysis was undertaken based on an adapted framework from the World Health Organization guide for “Monitoring and Evaluating Digital Health Interventions” and the Technology Acceptance Model (TAM). Findings Frontline health workers perceived the application to be highly usable and the use of Android phones for reporting was reported to be acceptable to the communities as long as photographic evidence was not collected. Increased workload due to both paper and digital reporting, occasional connectivity issues, and security issues with the use of mobile phones in certain areas were key primary barriers, whereas low motivation and increasing task load of frontline health workers were secondary issues reported. Supervisors and health managers perceived an improvement in the timeliness of data reporting by frontline health workers as well as more complete reporting. The app-collected data was perceived to facilitate data verification on the ground and managers were more confident of the reliability of digital reporting as compared to paper-based records. Conclusion: The use of the smartphone-based application has good acceptability among frontline health workers and their managers and was perceived to provide more reliable data timely data as compared to paper-based reporting benefits. The duplicative paper-based system, security in remote areas, and chronic issues with health worker programs are challenges that need to be encountered for embedding within the health system.
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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 | 0,033 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».