mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol
Bibliographic record
Abstract
<ns3:p>Background Mobile health (mHealth) tools are increasingly being used in Sub-Saharan Africa (SSA) to improve the quality of health services. mHealth clinical decision-making tools have several established roles in maternal and perinatal healthcare including health surveillance, data collection and access to guidelines. The adoption of mHealth clinical decision-making tools in low-resource environments like SSA, as well as the lessons learnt from using them, have not yet been determined. As new mHealth technologies are quickly being evaluated and deployed in resource-poor settings, it is crucial to thoroughly analyse what has been accomplished in order to inform implementers and policy makers on the effectiveness of technology in evidence-based practice. Objective This study aims to synthesize the available evidence 1) on the use of mHealth clinical decision-making tools for maternal and perinatal care in SSA, and 2) whether these tools lead to improvements in the quality of maternal and perinatal care in SSA. Methods A systematic review of the literature will be performed to identify publications describing the use mHealth tools for maternal and perinatal clinical decision-making in SSA. PubMed, CINAHL, EMBASE, Global Health and Web of Science will be searched for relevant articles following a predetermined search strategy with no date restrictions. A limited grey literature search will also be carried out. Two independent reviewers will screen the articles. Pre-determined data items will be extracted, and data synthesis carried out using a descriptive approach. Appraisal will be done using the Appraisal of Guidelines Research and Evaluation Health Systems (AGREE-HS) instrument. Conclusions This systematic review protocol for identifying and appraising mHealth clinical decision-making tools in maternal and perinatal care may help to establish best practice for developing and scaling up, thus help to improve care in SSA. Registration PROSPERO (CRD42023452760; 19 August 2023).:</ns3:p>
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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 | 0.052 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".