Adoption of mHealth Technologies by Community Health Workers to Improve the Use of Maternal Health Services in Sub-Saharan Africa: Protocol for a Mixed Method Systematic Review
Bibliographic record
Abstract
BACKGROUND: Studies have shown that mobile health technologies (mHealth) enhance the use of maternal health services. However, there is limited evidence of the impact of mHealth use by community health workers (CHWs) on the use of maternal health services in sub-Saharan Africa. OBJECTIVE: This mixed method systematic review will explore the impact of mHealth use by CHWs on the use of the maternal health continuum of care (antenatal care, intrapartum care, and postnatal care [PNC]), as well as barriers and facilitators of mHealth use by CHWs when supporting maternal health services. METHODS: We will include studies that report the impact of mHealth by CHWs on the use of antenatal care, facility-based births, and PNC visits in sub-Saharan Africa. We will search 6 databases (MEDLINE, CINAHL, Web of Science, Embase, Scopus, and Africa Index Medicus), with additional articles identified from Google Scholar and manual screening of references of the included studies. The included studies will not be limited by language or year of publication. After study selection, 2 independent reviewers will perform title and abstract screening, followed by full-text screening to identify the final papers to be included. Data extraction and risk-of-bias assessment will be performed using Covidence software by 2 independent reviewers. We will use a Mixed Methods Appraisal Tool to perform risk-of-bias assessments on all included studies. Finally, we will perform a narrative synthesis of the outcomes, integrating information about the effect of mHealth on maternal health use and barriers and facilitators of mHealth use. This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. RESULTS: In September 2022, we conducted an initial search in the eligible databases. After removing duplicates, we identified 1111 studies that were eligible for the title and abstract screening. We will finalize the full-text assessment for eligibility, data extraction, assessment of methodological quality, and narrative synthesis by June 2023. CONCLUSIONS: This systematic review will present new and up-to-date evidence on the use of mHealth by CHWs along the pregnancy, childbirth, and PNC continuum of care. We anticipate the results will inform program implementation and policy by highlighting the potential impacts of mHealth and presenting contextual factors that should be addressed to ensure the success of the programs. TRIAL REGISTRATION: PROSPERO CRD42022346364; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346364. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44066.
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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 | 0.131 | 0.136 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".