Effect of a community health worker mHealth monitoring system on uptake of maternal and newborn health services in Rwanda
Bibliographic record
Abstract
BACKGROUND: In an effort to improve access to proven maternal and newborn health interventions, Rwanda implemented a mobile phone (mHealth) monitoring system called RapidSMS. RapidSMS was scaled up across Rwanda in 2013. The objective of this study was to evaluate the impact of RapidSMS on the utilization of maternal and newborn health services in Rwanda. METHODS: Using data from the 2014/15 Rwanda demographic and health survey, we identified a cohort of women aged 15-49 years who had a live birth that occurred between 2010 and 2014. Using interrupted time series design, we estimated the impact of RapidSMS on uptake of maternal and newborn health services including antenatal care (ANC), health facility delivery and vaccination coverage. RESULTS: Overall, the coverage rate at baseline for ANC (at least one visit), health facility delivery and vaccination was very high (> 90%). The baseline rate was 50.30% for first ANC visit during the first trimester and 40.57% for at least four ANC visits. We found no evidence that implementing RapidSMS was associated with an immediate increase in ANC (level change: -1.00% (95% CI: -2.30 to 0.29) for ANC visit at least once, -1.69% (95% CI: -9.94 to 6.55) for ANC (at least 4 visits), -3.80% (95% CI: -13.66 to 6.05) for first ANC visit during the first trimester), health facility delivery (level change: -1.79, 95% CI: -6.16 to 2.58), and vaccination coverage (level change: 0.58% (95%CI: -0.38 to 1.55) for BCG, -0.75% (95% CI: -6.18 to 4.67) for polio 0). Moreover, there was no significant trend change across the outcomes studied. CONCLUSION: Based on survey data, the implementation of RapidSMS did not appear to increase uptake of the maternal and newborn health services we studied in Rwanda. In most instances, this was because the existing level of the indicators we studied was very high (ceiling effect), leaving little room for potential improvement. RapidSMS may work in contexts where improvement remains to be made, but not for indicators that are already very high. As such, further research is required to understand why RapidSMS had no impact on indicators where there was enough room for improvement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".