Challenge of virtual medical emergency education in conflict zone in low and middle-income countries: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Healthcare training in conflict‑affected low‑ and middle‑income countries is repeatedly disrupted by insecurity, displacement, and damaged infrastructure. Virtual education offers a practical route to sustain emergency care training when classrooms and clinical placements are curtailed, but the effectiveness of such programmes and the feasibility of delivery under conflict conditions remain uncertain. This protocol sets out methods to evaluate educational outcomes and implementation barriers for online emergency medical education in these settings. Methods: This will be a systematic review and, where appropriate, meta‑analysis registered prospectively with PROSPERO and aligned with PRISMA 2020. We will include randomised, quasi‑experimental, observational, qualitative, and mixed‑methods studies that evaluate virtual or online emergency education for healthcare professionals, trainees, students, or first responders in conflict‑affected low‑ and middle‑income countries. Primary outcomes will be knowledge and skill gain; secondary outcomes will include learner satisfaction and implementation barriers spanning technical, infrastructural, pedagogical, security, and cost domains. We will search PubMed, Embase, Scopus, and Web of Science for records from October 2022 to May 2025 with no language restrictions. Two reviewers will screen and extract data independently with third‑party adjudication, contacting authors for missing information. Risk of bias will be assessed using RoB 2, the Newcastle–Ottawa Scale, CASP, and MMAT as appropriate, and certainty of evidence with GRADE. When at least three comparable studies with quantitive outcomes are available, we will conduct random‑effects meta‑analysis using standardised mean differences with 95% confidence intervals; otherwise a SWiM‑guided narrative synthesis will be undertaken. Heterogeneity will be examined with I² alongside prespecified subgroup and sensitivity analyses. Discussion: The review will clarify the educational impact of virtual emergency training in conflict settings and identify context‑specific barriers, informing programme design, equitable access, and integration with resilient blended learning models. Registration: This protocol had been prospectively registered with PROSPERO (ID: CRD420251083001)
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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 | 0.087 | 0.112 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.032 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".