Experiences of women receiving mhealth-supported antenatal care in the village from community health workers in rural Burkina Faso, Africa
Bibliographic record
Abstract
OBJECTIVE: This qualitative study explored the experiences of women receiving mhealth-supported antenatal care in a village, from community health workers (CHWs) in rural Burkina Faso, Africa. INTERVENTION: CHWs entered patient clinical data manually in their smartphone during their home visits. All wireless transferred data was monitored by the midwives in the community clinic for arising medical complications. METHODS: Semi-structured interviews were conducted with 19 pregnant women, who were housewives, married and their age ranged from 18 to 39 years. None had completed their formal education. Depending on the weeks of gestation during their first antenatal care visit, length of enrollment in the project varied between three and eight months. Transcripts were content-analyzed. RESULTS: Despite the fact that mhealth was a novel service for all participants, they expressed appreciation for these interventions, which they found beneficial on three levels: 1) it allowed for early detection of pregnancy-related complications, 2) it was perceived as promoting collaboration between CHWs and midwives, and 3) it was a source of reassurance during a time when they are concerned about their health. Although not unanimous, certain participants said their husbands were more interested in their antenatal care as a result of these services. CONCLUSION: Findings suggested that mhealth-supported visits of the CHWs have the potential to increase mothers' knowledge about their pregnancy and, as such, motivate them to attend more ANC visits. In response to this increased patient engagement, midwives approached women differently, which led to the mothers' perception of improvement in the patient-provider relationship. Results also indicated that mhealth may increase spousal involvement, as services are offered at home, which is an environment where spouses feel more comfortable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".