Can digital prompting and the engagement of the husband influence the satisfaction of disadvantaged women with their reproductive health journey? A cross-sectional study from Lebanon
Bibliographic record
Abstract
Objective: Maternal health in Lebanon is severely impacted by the country's ongoing socioeconomic crisis, disproportionately affectivng disadvantaged Lebanese and refugee women due to limited healthcare access. Digital prompting interventions have improved antenatal and postnatal care utilization, particularly when the husband of the pregnant woman is also engaged. This study aims to assess the influence of digital prompting and husband engagement on the satisfaction of disadvantaged pregnant women in Lebanon with their reproductive health journeys, using the artificial intelligence (AI)-based gamified mHealth intervention titled "Gamification and Artificial Intelligence and mHealth Network for Maternal Health Improvement" (GAIN MHI). Methods: This study was conducted across seven primary healthcare centers in Lebanon, targeting pregnant women up to 16 weeks of gestation with mobile phone access. The intervention included digital messages for both pregnant women and their husbands, alongside the GAIN MHI App for healthcare providers. Over 11 months, data was collected to assess maternal satisfaction, antenatal care (ANC) attendance, and the role of husband engagement in supporting maternal wellbeing. Results: A total of 1028 pregnant women participated. Husband involvement significantly improved support for ANC visit, reminder's frequency, and psychological support. Women receiving mobile health support were more likely to report better physical health (odds ratio (OR) = 2.16; p = 0.03) and mental health (OR = 2.12; p = 0.03). Increased ANC visits were associated with higher likelihood of satisfaction with baby health (OR = 1.35; p = 0.05) and with service quality (OR = 2.68; p = 0.01). Husband support for ANC visits improved satisfaction both predelivery (OR = 2.15; p < 0.01) and postdelivery (OR = 2.05; p < 0.01). The combined effect of all support factors significantly boosted satisfaction with self-care predelivery (OR = 2.07; p < 0.01) and postdelivery (OR = 3.82; p < 0.01). Conclusion: The findings emphasize the importance of hybrid digital health models integrating mobile-based education, spousal support, and healthcare provider engagement to enhance maternal satisfaction and health outcomes. Future programs should adopt this approach to ensure comprehensive maternal care.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".