A Stakeholder-Centered mHealth Implementation Inquiry Within the Digital Health Innovation Ecosystem in South Africa: MomConnect as a Demonstration Case
Notice bibliographique
Résumé
BACKGROUND: The internet is a useful web-based multimedia platform for accessing and disseminating information unconstrained by time, distance, and place. To the health care sector's benefit, the advent and proliferation of mobile devices have provided an opportunity for interventions that combine asynchronous technology-aided health services to improve the lives of the less privileged and marginalized people and their communities, particularly in developing societies. OBJECTIVE: This study aimed to report on the perspectives of the different stakeholders involved in the study and to review an existing government mobile health (mHealth) program. It forms part of a study to design a re-engineered strategy based on the best demonstrated practices (considerations and methods) and learned experiences from the perspectives of multiple stakeholders within the digital health innovation ecosystem in South Africa. METHODS: This study used an ethnographic approach involving document review, stakeholder mapping, semistructured individual interviews, focus group discussions, and participant observations to explore, describe, and analyze the perspectives of its heterogeneous participant categories representing purposively sampled but different constituencies. RESULTS: Overall, 80 participants were involved in the study, in addition to the 6 meetings the researcher attended with members of a government-appointed task team. In addition, 46 archived records and reports were consulted and reviewed as part of gathering data relating to the government's MomConnect project. Among the consulted stakeholders, there was general consensus that the existing government-sponsored MomConnect program should be implemented beyond mere piloting, to as best as possible capacity within the available resources and time. It was further intimated that the scalability and sustainability of mHealth services as part of an innovative digital health ecosystem was hamstrung by challenges that included stakeholder mismanagement, impact assessment inadequacies, management of data, lack of effective leadership and political support, inappropriate technology choices, eHealth and mHealth funding, integration of mHealth to existing health programs in tandem with Goal 3 of the Sustainable Development Goals, integration of lessons learned from other mHealth initiatives to avoid resource wastage and duplication of efforts, proactive evaluation of both mHealth and eHealth strategies, and change management and developing human resources for eHealth. CONCLUSIONS: This study has only laid a foundation for the re-engineering of mHealth services within the digital health innovation ecosystem. This study articulated the need for stakeholder collaboration, such as continuous engagement among academics, technologists, and mHealth fieldwork professionals. Such compelling collaboration is accentuated more by the South African realities of the best practices in the fieldwork, which may not necessarily be documented in peer-reviewed or systematic research documents from which South African professionals, research experts, and practitioners could learn. Further research is needed for the retrospective analysis of mHealth initiatives and forecasting of the sustainability of current and future mHealth initiatives in South Africa.
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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 | 0,007 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,006 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,003 | 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 ».