ReviewJournal of medical education and curricular development
Exploring Optimal Group Sizes for Learning in Medical Simulation: A Systematic Review.
Review in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Supporting researchers to involve patient partners in health service research: developing and assessing the feasibility and acceptability of a co-produced training programme for researchers.Research involvement and engagement · 2026Article
- Medical Students' Perceptions of the Family Medicine Course at Yarmouk University: A Cross-Sectional Study.Advances in medical education and practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesSimulation is an effective teaching method that improves learner competence and confidence. Optimizing group size balances efficiency without sacrificing efficacy. While simulation technology is widely used in medical education, no standard for learner group size exists. This study investigates the optimal group size for simulation, aiming to identify best practices that maximize efficiency and efficacy in learning environments.
methodsThis systematic review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A team of emergency medicine educators screened citations and reviewed relevant full-text articles. Inclusion criteria focused on group sizes with the best outcomes. Quality assessment employed the Medical Education Research Study Quality Instrument approach to evaluate evidence.
resultsThirty-four articles were identified; 17 were deemed relevant for full-text review. The studies varied in methods, including prospective and retrospective reviews, mixed methods, and randomized controlled trials. Smaller groups improved outcomes, with an ideal size dependent on learning objectives. Five studies suggested groups of up to 4 learners were optimal, with mixed results on the exact number. One study identified 6 as the ideal group size. Debriefing was effective in larger groups, while procedural skills were best taught in groups of 2 to 4 learners.
conclusionThis review suggests smaller group sizes are more effective for efficiency, knowledge, and confidence. For procedural skills, groups of 2 to 4 are most effective, and effectiveness declines with more than 6 participants. Smaller groups allow for more hands-on learning and cognitive engagement. While clinical skills can be taught in larger groups, learners favor smaller groups for debriefing and complex scenarios. Effective curriculum planning should account for available resources, the type of simulation, and the material being taught, with group sizes adjusted to optimize learning outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.