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)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> We describe the protocol for adapting, expanding, and exponentially scaling the mMOM intervention <i>qualitatively</i> through adding COVID-19–related MNCH guidance and novel technological components (mobile app and artificial intelligence chatbots) and <i>quantitatively</i> through broadening the geographical area to reach exponentially more participants, within the evolving COVID-19 context. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> PRR1-10.2196/44720 </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".