Challenges and opportunities in the uptake of simulation in healthcare education in the developing world: a scoping review
Bibliographic record
Abstract
Background: Simulation is increasingly being adopted by healthcare educators throughout the developed world as it offers a safe environment to practice skills. While there is literature on learning via simulation in healthcare in the developed world, more studies are required to investigate the factors influencing this approach in the developing world. Objective: This scoping review highlights the key factors that act as deterrents as well as encouragement to the uptake of simulation as a teaching methodology in healthcare education in developing countries. Design: The MEDLINE (via OVID, using keywords and MeSH in OVID), and PubMed (via NCBI using MeSH), and CINAHL databases were searched between January 2000 and January 2024 for research articles published in peer reviewed English language journals using a combination of keywords. Results: A total of 48 articles were included in the final analysis. Challenges and opportunities were divided into professional, academic, and resource-based factors, and their individual sub-themes. The main challenges reported were the lack of a contextual curriculum, content heavy curricula, dearth of trained simulationists and cost of simulators. Performance anxiety was an important challenge reported by both trainers and trainees. Main opportunities were an interest in adopting simulation-based education from both trainers and trainees, and the opportunity to improve patient safety and quality of education. Other findings were that academic leadership and faculty show interest and urgency to adopt simulation in curricula and allocate funds for this. Facilitators need to be provided with protected time to become simulationists. Local manufacturers need to be sourced for simulators, and transfer of technology and expertise needs to be negotiated. Conclusion: Simulation needs to be looked at from the lens of not only education, but more importantly of patient safety in developing countries to allow simulation-based education to be mainstreamed in health professions education in low- and middle-income contexts.
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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 | 0.029 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".