Adapting and Scaling a Digital Health Intervention to Improve Maternal and Child Health Among Ethnic Minority Women in Vietnam Amid the COVID-19 Context: Protocol for the dMOM Project (Preprint)
Notice bibliographique
Résumé
BACKGROUND Due to interconnected structural determinants including low maternal health knowledge, economic marginalization, and remoteness from low-capacity health centers, ethnic minority women in remote areas of Vietnam face severe maternal, newborn, and child health (MNCH) inequities. As ethnic minorities represent 15% of the Vietnamese population, these disparities are significant. mMOM—a pilot mobile health (mHealth) intervention using SMS text messaging to improve MNCH outcomes among ethnic minority women in northern Vietnam—was implemented from 2013-2016 with promising results. Despite mMOM’s findings, exacerbated MNCH inequities, and digital health becoming more salient amid COVID-19, mHealth has not yet been scaled to address MNCH among ethnic minority women in Vietnam. OBJECTIVE We describe the protocol for adapting, expanding, and exponentially scaling the mMOM intervention qualitatively through adding COVID-19–related MNCH guidance and novel technological components (mobile app and artificial intelligence chatbots) and quantitatively through broadening the geographical area to reach exponentially more participants, within the evolving COVID-19 context. METHODS dMOM will be conducted in 4 phases. (1) Drawing on a review of international literature and government guidelines on MNCH amid COVID-19, mMOM project components will be updated to respond to COVID-19 and expanded to include a mobile app and artificial intelligence chatbots to more deeply engage participants. (2) Using an intersectionality lens and participatory action research approach, a scoping study and rapid ethnographic fieldwork will explore ethnic minority women’s unmet MNCH needs; acceptability and accessibility of digital health; technical capacity of commune health centers; gendered power dynamics and cultural, geographical, and social determinants impacting health outcomes; and multilevel impacts of COVID-19. Findings will be applied to further refine the intervention. (3) dMOM will be implemented and incrementally scaled across 71 project communes. (4) dMOM will be evaluated to assess whether SMS text messaging or mobile app delivery engenders better MNCH outcomes among ethnic minority women. The documentation of lessons learned and dMOM models will be shared with Vietnam’s Ministry of Health for adoption and further scaling up. RESULTS The dMOM study was funded by the International Development Research Centre (IDRC) in November 2021, cofacilitated by the Ministry of Health, and is being coimplemented by provincial health departments in 2 mountainous provinces. Phase 1 was initiated in May 2022, and phase 2 is planned to begin in December 2022. The study is expected to be complete in June 2025. CONCLUSIONS dMOM research outcomes will generate important empirical evidence on the effectiveness of leveraging digital health to address intractable MNCH inequities among ethnic minority women in low-resource settings in Vietnam and provide critical information on the processes of adapting mHealth interventions to respond to COVID-19 and future pandemics. Finally, dMOM activities, models, and findings will inform a national intervention led by the Ministry of Health. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/44720
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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 | 0,024 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,131 | 0,020 |
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 ».