Development of a Maternal, Newborn and Child mHealth Intervention in Thai Nguyen Province, Vietnam: Protocol for the mMom Project
Bibliographic record
Abstract
BACKGROUND: Ethnic minority women (EMW) living in mountainous areas of northern Vietnam have disproportionately high infant and maternal mortality rates as a result of low maternal health knowledge, poverty, and remoteness from low-capacity health centers. OBJECTIVE: The objective of this study was to describe the protocol for the development and evaluation of the mMom intervention, which is an integrated mobile health (mHealth) system designed to improve maternal and infant health knowledge, and behavior among women in remote areas of Thai Nguyen, Vietnam. METHODS: This project featured the following four phases: (1) development of an mHealth platform integrated into the existing health management information system in partnership with the provincial health department; (2) ethnographic fieldwork and intervention content development; (3) intervention piloting and implementation; and (4) evaluation of the intervention's impact on participants' maternal health knowledge, behavior, and interactions with the health system. RESULTS: The mMom project development process resulted in the following: (1) the successful development of the mMom system, including the mHealth platform hardware and integration, the intervention plan and content, and the monitoring and evaluation framework; (2) the piloting and implementation of the intervention as planned; and (3) the implementation of the monitoring and evaluation framework components. CONCLUSIONS: This protocol outlines the development of the mMom intervention and describes critical next steps in understanding the impact of the intervention on participants and the wider health system in Thai Nguyen province, Vietnam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".