The Mobile Alliance for Maternal Action Text Message–Based mHealth Intervention for Maternal Care in South Africa: Qualitative User Study
Bibliographic record
Abstract
BACKGROUND: Using mobile technology to support health care (mobile health [mHealth]) has been shown to improve health outcomes across a multitude of health specialties and across the world. Exploring mHealth user experiences can aid in understanding how and why an intervention was successful. The Mobile Alliance for Maternal Action (MAMA) was a free maternal mHealth SMS text messaging service that was offered to pregnant women in Johannesburg, South Africa, with the goal of improving maternal, fetal, and infant health outcomes. We conducted focus group discussions with MAMA users to learn about their experiences with the program. OBJECTIVE: The aim of this qualitative study was to gather opinions of participants of the MAMA maternal mHealth service regarding health care atmosphere, intervention use, and intervention feedback. METHODS: Prenatal and postnatal women (N=15) from public antenatal and postnatal care sites in central Johannesburg who were receiving free maternal health text messages (MAMA) participated in 3 focus group discussions. Predefined discussion topics included personal background, health care system experiences, MAMA program recruitment, acceptability, participant experiences, and feedback. RESULTS: The feedback regarding experiences with the health system were comprised of a few reports of positive experiences and many more reports of negative experiences such as long wait times, understaffed facilities, and poor service. Overall acceptability for the maternal text message intervention was high. Participants reflected that the messages were timely, written clearly, and felt supportive. Participants also reported sharing messages with friends and family. CONCLUSIONS: These findings suggest that maternal mHealth interventions delivered through text messages can provide timely, relevant, useful, and supportive information to pregnant women and new mothers especially in settings where there may be mistrust of the health care system.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".