Texting for life: a mobile phone application to connect pregnant women with emergency transport and obstetric care in rural Nigeria
Bibliographic record
Abstract
Difficulty in transportation to access skilled providers has been cited repeatedly as a major barrier to utilization of emergency obstetric care in Nigeria.The objective of this paper is to describe the design, implementation, and outcomes of a mobile phone technology aimed at rapidly reaching rural Nigerian women who experience pregnancy complications with emergency transportation and access to providers.The project was implemented in 20 communities in two predominantly rural Local Government Areas (LGAs) of Edo State, in southern Nigeria, as part of a larger implementation project aimed at improving the access of rural women to skilled pregnancy care. The digital health innovation named Text4Life, allowed women to send a brief message from their mobile phone to a server linked to Primary Health Care (PHC) facilities and to access pre-registered transport owners. Pregnant women were registered and taught to text short messages to a server from their mobile phones or those of a friend or relative when they experience complications.Over 18 months, 56 women out of 1620 registered women (3.5%) texted the server requesting emergency transportation. Of this number, 51 were successfully transported to the PHC facilities, 46 were successfully treated at the PHC, and five were referred to higher-level care facilities. No maternal deaths occurred during the period, while four perinatal deaths were recorded.We conclude that a rapid short message sent from a mobile phone to a central server and connected to transport providers and health facility managers is effective in increasing the access of pregnant women to skilled emergency obstetric services in rural Nigeria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".