Design Opportunities for Persuasive Mobile Apps to Support Maternal and Child Healthcare and Help-seeking Behaviors
Bibliographic record
Abstract
Electronic health (e-health) interventions have been used to provide maternal and child healthcare services in low-middle-income countries of the world. Despite the potential benefits of such healthcare interventions, there have been varying degrees of successes reports in their implementations; the mortality rate before and during childbirth and postpartum is still on the rise in rural Africa. This increase may not be unconnected to some factors that adversely influence women from seeking appropriate maternal and child healthcare services. Moreover, a review of existing e-health interventions uncover that there are no specific operationalizations of relevant persuasive strategies which have the capacity to motivate the adoption of appropriate maternal health-seeking behaviors. Therefore, we advance research in this direction by examining factors that lead to inappropriate maternal health-seeking behaviors amongst expectant and nursing mothers in developing nations. The findings from a large-scale user study of 600 participants from Africa uncover that factors such as cultural practices, religious beliefs, remoteness and inaccessibility and long waiting times at the health facility, ignorance and negative perception, continue to pose a big challenge to maternal and child health care. We reflected on the findings and mapped them to their remedial persuasive strategies while offering socially and culturally-sensitive design guidelines for tailoring Persuasive Mobile Apps to motivate Maternal and Child Healthcare and Help-seeking Behaviors amongst users in the Global South.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".