The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda
Bibliographic record
Abstract
Maternal and child mortality rates remain unacceptably high globally, particularly in sub-Saharan Africa. A popular approach to counter these high rates is interventions delivered using mobile phones (mHealth). However, few mHealth interventions have been implemented nationwide and there has been little evaluation of their effectiveness, particularly at scale. Therefore, we evaluated the Rwanda RapidSMS programme-one of the few mHealth programmes in Africa that is currently operating nationwide. Using interrupted time series analysis and monthly data routinely reported by public health centres (n = 461) between 2012 and 2016, we studied the impact of RapidSMS on four indicators: completion of four antenatal care visits, deliveries in a health facility, postnatal care visits and malnutrition screening. We stratified all analyses based on whether the district received concurrent additional supports, including staff and equipment (10 out of 30 Districts). We found that community health workers in Rwanda sent more than 9.3 million messages using RapidSMS, suggesting the programme was successfully implemented. We found that the implementation of the RapidSMS system combined with additional support including training, supervision and equipment provision increased the use of maternal and child health services. In contrast, implementing the RapidSMS system alone was ineffective. This suggests that mHealth programmes alone may be insufficient to improve the use of health services. Instead, they should be considered as a part of more comprehensive interventions that provide the necessary equipment and health system capacity to support them.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".