Enablers and barriers to the acceptability of mHealth for maternal healthcare in rural Edo, Nigeria
Bibliographic record
Abstract
Objective: Acceptability has become a key consideration in designing, implementing and evaluating digital health interventions. Current evidence points to acceptability as a crucial factor in sustaining mobile health programs for maternal health across sub-Saharan Africa particularly in Nigeria where the burden of maternal mortality is high. This paper describes the enablers and barriers to the acceptance of Text4Life, a mobile phone-based health intervention that extends maternal healthcare services to rural areas of Edo State Nigeria. Method: This is a cross-sectional qualitative study of women who used Text4Life, their spouses who were all men and Ward Development Committee chairpersons who oversaw the implementation of Text4Life. This study was set in Etsako East and Esan Central Local Government Areas of Edo State, Nigeria. Between September 2021 and January 2022, eight focus groups were conducted with 64 participants: 39 women and 25 men. Two in-depth interviews were conducted with Ward Development Committee chairpersons. Data collection was conducted in English and Pidgin English. Discussions and interviews were digitally recorded and translated to English from Pidgin English where necessary. Data analysis followed a mainly deductive approach to thematic analysis, however, emergent information from the data was also considered and reported. Results: The results show that participants' positive attitudes towards the intervention, the involvement of the community, participants' understanding of the intervention, and perceived effectiveness of the Text4Life program were enablers to women's acceptance of Text4Life and enablers to Ward Development Committee chairpersons' assistance with the program. On the other hand, limited resources and a clash with the community's value system presented barriers to the acceptability of the Text4Life program. Conclusion: Our findings demonstrate the importance of alleviating the burdens associated with participating in mobile health interventions while noting that the risk of obstructing the gains from mobile health interventions is high if plans for sustaining it are not incorporated early enough in the design phase.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".