Mobile health technology in providing maternal health services – Awareness and challenges faced by pregnant women in upper West region of Ghana
Bibliographic record
Abstract
Objectives: The study assessed awareness on Mobile Health (mhealth) Technology as well as challenges pregnant women encounter in the utilization of mhealth technology to improve maternal health in rural settings in the Upper West Region (UWR) of Ghana. Study design: The study was an exploratory design that employed the qualitative method of data collection. Methods: Semi-structured interview guide was used to conduct six (6) Focus Group Discussions (FGDs) and nine (9) Key Informant Interviews (KIIs) among pregnant women and health workers respectively from three (3) selected rural districts in the Upper West Region. Data was collected in August 2020. Thematic analysis was conducted and some statements from participants were presented verbatim to illustrate the themes realized. Results: Participants were aware of the mhealth intervention that had been implemented by Savanna Signatures in their districts. Major sources of information on the mhealth services were from durbars, health education sessions and health care providers. Challenges faced by pregnant women, in the mhealth technology intervention were; financial challenges, lack of mobile network connectivity, lack of electricity in some rural areas, low female literacy rate at household level and cultural barriers. Conclusion: The Savanna Signatures mhealth intervention is widely known but some challenges exist that impede the smooth implementation of the intervention. The mhealth technology intervention implementers should partner with other sectors and policy makers to address the challenges identified by the study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".