A mixed methods systematic review of success factors of mhealth and telehealth for maternal health in Sub-Saharan Africa
Bibliographic record
Abstract
Abstract: Access to health care is still limited for many women in sub-Saharan Africa (SSA), while it remains an important determinant of maternal mortality and morbidity. Information and communication technologies (ICTs), such as mhealth and telehealth, can help to facilitate this access by acting on the various obstacles encountered by women, be they socio-cultural, economic, geographical or organizational. However, various factors contribute to the success of mhealth and telehealth implementation and use, and must be considered for these technologies to go beyond the pilot project stage. The objective of this systematic literature review is to synthesize the empirical knowledge on the success factors of the implementation and use of telehealth and mhealth to facilitate access to maternal care in SSA. The methodology used is based on that of the Cochrane Collaboration, including a documentary search using standardized language in six databases, selection of studies corresponding to the inclusion criteria, data extraction, evaluation of study quality, and synthesis of the results. A total of 93 articles were identified, which allowed the inclusion of seven studies, six of which were on mhealth. Based on the framework proposed by Broens et al., we synthesized success factors into five categories: (I) technology, such as technical support to maintain, troubleshoot and train users, good network coverage, existence of a source of energy and user friendliness; (II) user acceptance, which is facilitated by factors such as unrestricted use of the device, perceived usefulness to the worker, adequate literacy, or previous experience of use ; (III) short- and long-term funding; (IV) organizational factors, such as the existence of a well-organized health system and effective coordination of interventions; and (V) political or legislative aspects, in this case strong government support to deploy technology on a large scale. Telehealth and mhealth are promising solutions to reduce maternal morbidity and mortality in SSA, but knowledge on how these interventions can succeed and move to scale is limited. Success factors identified in this review can provide guidance on elements that should be considered in the design and implementation of telehealth and mhealth for maternal health 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 | 0.039 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".