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 healthcare financing are critical determinants of 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. There is a shortage of information about the feasibility of using a custom-made mobile technology that integrates smart travel and mHealth. This paper explores the feasibility of implementing a custom-made geographically enabled mobile technology-based tool (RoadMApp) to counter the adverse effects of long travel times for maternal care in Kwekwe District, Zimbabwe.Methods: We frame the paper using the first two steps (listen & plan) of the Spiral Technology Action Research (STAR model).The paper uses an exploratory case study design and Participatory Learning Approaches (PLA) with stakeholders (community members) and in-depth interviews with key informants (health care service providers, pregnant women, transport operators). 193 participants took part in the study. We conducted focus group discussions with pregnant women, women of childbearing age, men (household heads), and elderly women. The discussion questions centered on travel time, availability of transport, cellular network coverage, and perceptions of the RoadMApp application. Data were analysed thematically using Nvivo Pro 12.Results: Most parts of rural Kwekwe are far from health facilities and have 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 mHealth, the implementation will probably be a challenge.Conclusion: Communities are keen to embrace the RoadMApp application. However, the feasibility of implementing RoadMApp in Kwekwe District will be a challenge because of maternal health care barriers such as poor road network, poor phone network, and the high cost of transport. There is a need to investigate the social determinants of access to maternity services to inform RoadMApp implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".