Use of mobile phones for behavior change communication to improve maternal, newborn and child health: a scoping review
Bibliographic record
Abstract
BACKGROUND: Behavior change communication (BCC) to improve health and caring practices is an integral component of efforts to improve maternal, newborn and child health (MNCH). Mobile phones are widely available in low- and middle-income countries (LMIC), presenting new opportunities for BCC delivery. There is need for delivery science to determine how best to leverage mobile phone technology for BCC to improve MNCH practices. METHODS: We conducted a scoping review of studies and project reports documenting the feasibility, implementation or effectiveness of using mobile phones for BCC delivery related to MNCH in LMIC. Data were extracted and synthesized from three sources: i) systematic search of three electronic databases (PubMed, MedLine, Scopus); ii) grey literature search, including mHealth databases and websites of organizations implementing mHealth projects; iii) consultation with researchers and programme implementers. Records were screened using pre-determined inclusion criteria and those selected were categorized according to their primary intervention delivery approaches. We then performed a descriptive analysis of the evidence related to both effectiveness and implementation for each delivery approach. RESULTS: The systematic literature search identified 1374 unique records, 64 of which met inclusion criteria. The grey literature search added 32 records for a total of 96 papers in the scoping review. Content analysis of the search results identified four BCC delivery approaches: direct messaging, voice counseling, job aid applications and interactive media. Evidence for the effectiveness of these approaches is growing but remains limited for many MNCH outcomes. The four approaches differ in key implementation elements, including frequency, length and complexity of communication, and potential for personalization. These elements influence resource allocation and are likely to impact effectiveness for BCC targeting complex, habitual MNCH practices. CONCLUSIONS: This scoping review contributes to the evidence-base on the opportunities and limitations of using mobile phones for BCC delivery aiming to improve MNCH practices. The incorporation of mobile phone technology in BCC interventions should be guided by formative research to match both the content and delivery approach to the local context. We recommend five areas for further research, including both effectiveness and implementation studies on specific delivery approaches.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".