Understanding gender dynamics in mHealth interventions can enhance the sustainability of benefits of digital technology for maternal healthcare in rural Nigeria
Bibliographic record
Abstract
Introduction: Nigeria faces enormous challenges to meet the growing demands for maternal healthcare. This has necessitated the need for digital technologies such as mobile health, to supplement existing maternal healthcare services. However, mobile health programs are tempered with gender blind spots that continue to push women and girls to the margins of society. Failure to address underlying gender inequalities and unintended consequences of mobile health programs limits its benefits and ultimately its sustainability. The importance of understanding existing gender dynamics in mobile health interventions for maternal health cannot be overstated. Objective: This study explores the gender dimensions of Text4Life, a mobile health intervention for maternal healthcare in Edo State, Nigeria by capturing the unique perspectives of women who are the primary beneficiaries, their spouses who are all men, and community leaders who oversaw the implementation and delivery of the intervention. Method: This qualitative study used criterion-based purposive sampling to recruit a total of 66 participants: 39 women, 25 men, and two ward development committee chairpersons. Data collection involved 8 age and sex desegregated focus group discussions with women and men and in-depth interviews with ward development committee chairpersons in English or Pidgin English. Translated and transcribed data were exported to NVivo 1.6 and data analysis followed a conventional approach to thematic analysis. Results: Women had some of the necessary resources to participate in the Text4Life program, but they were generally insufficient thereby derailing their participation. The program enhanced women's status and decision-making capacity but with men positioned as heads of households and major decision-makers in maternal healthcare, there remained the possibility of deprioritizing maternal healthcare. Finally, while Text4Life prioritized women's safety in various contexts, it entrenched systems of power that allow men's control over women's reproductive lives. Conclusion: As communities across sub-Saharan Africa continue to leverage the use of mHealth for maternal health, this study provides insights into the gender implications of women's use of mHealth technologies. While mHealth programs are helpful to women in many ways, they are not enough on their own to undo entrenched systems of power through which men control women's access to resources and their reproductive and social lives.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".