Mobile-Social Learning for Continuing Professional Development in Low- and Middle-Income Countries: Integrative Review
Bibliographic record
Abstract
BACKGROUND: Access to continuing professional development (CPD) for health care workers in low- and middle-income countries (LMICs) is severely limited. Digital technology serves as a promising platform for supporting CPD for health care workers by providing educational content virtually and enabling virtual peer-to-peer and mentor interaction for enhanced learning. Digital strategies for CPD that foster virtual interaction can increase workforce retention and bolster the health workforce in LMICs. OBJECTIVE: The objective of this integrative review was to evaluate the evidence on which digital platforms were used to provide CPD to health care workers and clinical students in LMICs, which was complemented with virtual peer-to-peer or mentor interaction. We phrased this intersection of virtual learning and virtual interaction as mobile-social learning. METHODS: A comprehensive database and gray literature search was conducted to identify qualitative, quantitative, and mixed methods studies, along with empirical evidence, that used digital technology to provide CPD and virtual interaction with peers or mentors. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Eligible articles were written in English, conducted in an LMIC, and used a mobile device to provide CPD and facilitate virtual peer-to-peer or mentor interaction. Titles, abstracts, and full texts were screened, followed by an assessment of the quality of evidence and an appraisal of the articles. A content analysis was then used to deductively code the data into emerging themes. RESULTS: A total of 750 articles were identified, and 31 (4.1%) were included in the review. SMS text messaging and mobile instant messaging were the most common methods used to provide continuing education and virtual interaction between peers and mentors (25/31, 81%). Across the included articles, participants had high acceptability for using digital platforms for learning and interaction. Virtual peer interaction and mentorship were found to contribute to positive learning outcomes in most studies (27/31, 87%) through increased knowledge sharing, knowledge gains, improved clinical skills, and improved service delivery. Peer-to-peer and mentor interaction were found to improve social support and reduce feelings of isolation (9/31, 29%). There were several challenges in the implementation and use of digital technology for mobile-social learning, including limited access to resources (eg, internet coverage and stable electricity), flexibility in scheduling to participate in CPD, and sociobehavioral challenges among students. CONCLUSIONS: The summary suggests that mobile-social learning is a useful modality for curriculum dissemination and skill training and that the interface of mobile and social learning serves as a catalyst for improved learning outcomes coupled with increased social capital.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".