Situating mobile health: a qualitative study of mHealth expectations in the rural health district of Nouna, Burkina Faso
Bibliographic record
Abstract
BACKGROUND: The implementation of mobile health (mHealth) projects in low- and middle-income countries raises high and well-documented expectations among development agencies, policymakers and researchers. By contrast, the expectations of direct and indirect mHealth users are not often examined. In preparation for a proposed intervention in the Nouna Health District, in rural Burkina Faso, this study investigates the expected benefits, challenges and limitations associated with mHealth, approaching these expectations as a form of situated knowledge, inseparable from local conditions, practices and experiences. METHODS: The study was conducted within the Nouna Health District. We used a qualitative approach, and conducted individual semi-structured interviews and group interviews (n = 10). Participants included healthcare workers (n = 19), godmothers (n = 24), pregnant women (n = 19), women with children aged 12-24 months (n = 33), and women of childbearing age (n = 92). Thematic and content qualitative analyses were conducted. RESULTS: Participants expect mHealth to help retrieve patients lost to follow-up, improve maternal care monitoring, and build stronger relationships between pregnant women and primary health centres. Expected benefits are not reducible to a technological realisation (sending messages), but rather point towards a wider network of support. mHealth implementation is expected to present considerable challenges, including technological barriers, organisational challenges, gender issues, confidentiality concerns and unplanned aftereffects. mHealth is also expected to come with intrinsic limitations, to be found as obstacles to maternal care access with which pregnant women are confronted and on which mHealth is not expected to have any significant impact. CONCLUSIONS: mHealth expectations appear as situated knowledges, inseparable from local health-related experiences, practices and constraints. This problematises universalistic approaches to mHealth knowledge, while nevertheless hinting at concrete, expected benefits. Findings from this study will help guide the design and implementation of mHealth initiatives, thus optimising their chances for success.
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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 | 0.040 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".