The mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review
Bibliographic record
Abstract
INTRODUCTION: mobile Health (mHealth) refers to use of mobile wireless technologies for health. The potential for mHealth to enhance healthcare delivery is supported by near-universal availability of mobile phones and their expanding coverage in low- and middle-income countries. This systematic review analyses the available evidence on mHealth clinical decision-making tools in maternal and perinatal health, and whether they lead to improved maternal and perinatal health outcomes in Sub-Saharan Africa (SSA). METHODS: Eligibility criteria: Studies conducted in SSA describing mHealth tools piloted or used for clinical decision-making in maternal or perinatal healthcare. Exclusion criteria included mHealth tools used outside of maternal and perinatal healthcare, publications lacking sufficient detail (where information couldn't be obtained through contacting authors), articles where tools were used on a laptop or desktop computer, and articles not published in English. Data sources: PubMed, CINAHL, EMBASE, Global Health, and Web of Science were searched for relevant articles following a predetermined search strategy with no date restrictions. A limited grey literature search was conducted. Risk of bias: We assessed the quality of included studies using the Cochrane Risk of bias 2 tool, Newcastle- Ottawa scale and COREQ. This comprehensive approach ensured a rigorous evaluation of bias and validity in our systematic review. Data extraction and synthesis: Two independent reviewers screened articles and extracted data. RESULTS: 1119 records were screened, and 36 articles met the inclusion criteria. Fifteen mHealth tools were identified across 11 SSA countries. CONCLUSION: mHealth tools for clinical decision-making in maternal and perinatal care were found to be feasible, usable, and acceptable. They demonstrated adequate user satisfaction, and some demonstrated improvement of pregnancy outcomes. However, technologies lack scalability, with only one scaled up nationally, and few tools interacted with existing health information systems or had plans for sustainability. This review will help establish best practice for developing and scaling up mHealth clinical decision-making tools, helping to improve maternal and perinatal healthcare in SSA.
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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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".