The Influence of Design and Implementation Characteristics on the Use of Maternal Mobile Health Interventions in Kenya: Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: The growth of mobile technology in developing countries, coupled with pressing maternal health care challenges, has led to a widespread implementation of maternal mobile health (mHealth) innovations. However, reviews generating insights on how the characteristics of the interventions influence use are scarce. OBJECTIVE: This study aims to review maternal mHealth interventions in Kenya to explore the influence of intervention design and implementation characteristics on use by maternal health clients. We also provide a starting inventory for maternal mHealth interventions in the country. METHODS: Using a systematic approach, we retrieved a total of 1100 citations from both peer-reviewed and gray sources. Articles were screened on the basis of an inclusion and exclusion criterion, and the results synthesized by categorizing and characterizing the interventions presented in the articles. The first phase of the literature search was conducted between January and April 2019, and the second phase was conducted between April and June 2021. RESULTS: A total of 16 articles were retrieved, comprising 13 maternal mHealth interventions. The study highlighted various mHealth design and implementation characteristics that may influence the use of these interventions. CONCLUSIONS: In addition to elaborating on insights that would be useful in the design and implementation of future interventions, this study contributes to a local inventory of maternal mHealth interventions that may be useful to researchers and implementers in mHealth. This study highlights the need for explanatory studies to elucidate maternal mHealth use, while complementing existing evidence on mHealth effectiveness.
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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 | 0.043 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 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".