Adoption of Mobile Phone Messages for Delivery and Newborn Care in Bangladesh
Bibliographic record
Abstract
In Bangladesh, mobile phones have been adopted as a health communication tool to improve maternal and child healthcare. To understand what works and what doesn’t work for mobile phone based messages around delivery, postpartum and newborn care in resource limited settings we interviewed 33 women who enrolled in an educational service that provides twice weekly voice or text messages to pregnant women and mothers of 0-11 month old babies and had participated in a survey. This follow up qualitative exploratory study showed that women appreciated receiving messages around newborn care and nutrition information over pregnancy messages. Women in low- income households faced challenges accessing messages on shared phones while those with low literacy and limited technological knowledge preferred to receive voice messages over text messages. Husband’s endorsement of the service improved women’s adoption of the messages. Knowledge on additional consultation service and information on how to enroll in the service for later pregnancies was found to be low. Some participants were reluctant to pay for educational messages and avoided the calls. Women’s healthcare practices suggested growing awareness on biomedical practices although women from low- income households were more likely to follow traditional unskilled birthing and newborn care practices at cultural influences unless experienced complications. Besides providing contextual messages, a holistic response is required that includes; training local birth attendants, sensitizing female family members who organize the home based deliveries, and establishing a subsidized referral system to improve birth related health outcomes in low- income households.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".