Development of a digital platform for the equitable promotion of mother and child health among marginalized populations: Formative study from a lower-middle income country
Bibliographic record
Abstract
ABSTRACT Background Health inequities exist between and within countries and communities for maternal and child health, nutrition, and early childhood development. Socially excluded bear the major brunt of this disparity. Innovative ways of providing healthcare are required to meet the needs of such populations. We report the development and feasibility testing of Sehat Ghar (Health House), an android-based digital application for volunteer health workers from a population not covered by Primary Healthcare (PHC). Methods We carried out a mixed-methods study with three steps. First, we conducted 13 in-depth interviews and two Focus Group Discussions (FGDs) with stakeholders to explore the gaps in community knowledge and practices. To address these gaps, we developed the Sehat Ghar App, comprised of video-based health education to demonstrate practices that mothers and families need to adopt. Second, we trained ten volunteer Community Health Workers (CHWs) from the same community to deliver health education using the App, and assessed their knowledge and skill improvement. Third, these CHWs visited pregnant and lactating mothers at home, that we monitored using a structured observation list. Results Initial exploration revealed a need for health-related knowledge and suboptimal healthcare utilization from neighbouring public hospitals. Sehat Ghar employed behaviour change techniques, including knowledge transfer, improving mothers’ self-efficacy, and enhanced family involvement in mother and childcare to address this. Volunteer women were trained from the community, who, after the training, showed a significant improvement in mean knowledge score [Before: M = 8.00 ( SD = 1.49), After: M = 11.40 ( SD = 1.43), p=.0007]. Our monitoring found these CHWs excellent in their interaction with mothers and excellent or very good in using the App. The CHW and her community reported their liking and satisfaction with the App and wanted its delivery on a regular basis. Conclusions The digital application Sehat Ghar is a simple, easy-to-use resource for CHWs and is acceptable to the community. Mothers appreciate the content and presentation and are ready to incorporate its messages into their daily practices. The real-world effectiveness of the innovation is currently being tested on 250 mother-infant pairs. With its usefulness and adaptability, and the rapidly spreading mobile phone and Internet technology, the innovation can educate communities at a large scale in a minimum amount of time, contributing to equitable coverage of health services in resource-constrained settings.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".