Socio-cultural contextual factors that contribute to the uptake of a mobile health intervention to enhance maternal health care in rural Senegal
Bibliographic record
Abstract
BACKGROUND: Although considerable progress has been made in reducing maternal mortality over the past 25 years in Senegal, the national maternal mortality ratio (MMR), at 315 deaths per 100,000 live births, is still unacceptably high. In recent years a mobile health (mHealth) intervention to enhance maternal health care has been introduced in rural and remote areas of the country. CommCare is an application that runs on cell phones distributed to community health workers known as matrones who enroll and track women throughout pregnancy, birth and the post-partum, offering health information, moral support, appointment reminders, and referrals to formal health care providers. METHODS: An ethnographic study of the CommCare intervention and the larger maternal health program into which it fits was conducted in order to identify key social and cultural contextual factors that contribute to the uptake and functioning of this mHealth intervention in Senegal. Ethnographic methods and semi-structured interviews were used with participants drawn from four categories: NGO field staff (n = 16), trained health care providers (including physicians, nurses, and midwives) (n = 19), community level health care providers (n = 13); and women belonging to a community intervention known as the Care Group (n = 14). Data were analyzed using interpretive analysis informed by critical medical anthropology theory. RESULTS: The study identified five socio-cultural factors that work in concert to encourage the uptake and use of CommCare: convening women in the community Care Group; a cultural mechanism for enabling pregnancy disclosure; constituting authoritative knowledge amongst women; harnessing the roles of older women; and adding value to community health worker roles. We argue that, while CommCare is a powerful tool of information, clinical support, surveillance, and data collection, it is also a social technology that connects and motivates people, transforming relationships in ways that can optimize its potential to improve maternal health care. CONCLUSIONS: In Senegal, mHealth has the potential not only to bridge the gaps of distance and expertise, but to engage local people productively in the goal of enhancing maternal health care. Successful mHealth interventions do not work as 'magic bullets' but are part of 'assemblages' - people and things that are brought together to accomplish particular goals. Attention to the social and cultural elements of the global health assemblage within which CommCare functions is critically important to understand and develop this mHealth technology to its full potential.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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, 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".