RoadMApp: A feasibility study for a smart travel application to improve maternal health delivery in a low resource setting in Zimbabwe
Bibliographic record
Abstract
Abstract Background : Travel time and health care financing are key determinants to the provision of quality maternal health care in low resource settings. Despite the availability of pregnancy-related mHealth and smart travel applications, there is a lack of evidence on their usage to travel to health facilities for routine antenatal care and emergencies in low resource settings like Zimbabwe. Little is known about the feasibility of the usage of custom-made mobile technologies that integrate smart travel and mHealth. This paper explores the feasibility of implementing a custom-made geographically enabled mobile technology-based tool (RoadMApp) to counter the negative effects of long travel times and poor financing strategies for maternal care in Kwekwe District, Zimbabwe. Methods : Focus group discussions were conducted with pregnant women, women of childbearing age, men (household heads) and elderly women. Participatory learning approaches with stakeholders (community members) and in-depth interviews with key informants (health care service providers, transport operators) were utilised. In total 193 people took part in the study. The discussion questions centred on travel time, availability of transport, cellular network coverage and perceptions of the RoadMApp application. Data was analysed thematically using Nvivo Pro 12. Results : Most parts of rural Kwekwe have long distances to health facilities and an inefficient road and telecommunications network. Hence, it is hard to predict if RoadMApp will integrate into the lives of the community - especially those in rural areas. Since these issues are pillars of the design of the RoadMApp application, the implementation is likely to be challenging. Conclusion : Communities are keen to embrace the RoadMApp application. However, the feasibility of implementing RoadMApp in Kwekwe District will be challenging due to maternal health care barriers such as poor road network, poor phone network and the high cost of transport. There is, therefore, a need to investigate the social determinants of access to maternity services in order to inform the RoadMApp implementation.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| 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".