The effective group size for teaching cardiopulmonary resuscitation skills – A randomized controlled simulation trial
Bibliographic record
Abstract
AIM OF THE STUDY: The ideal group size for effective teaching of cardiopulmonary resuscitation is currently under debate. The upper limit is reached when instructors are unable to correct participants' errors during skills practice. This simulation study aimed to define this limit during cardiopulmonary resuscitation teaching. METHODS: Medical students acting as simulated Basic Life Support course participants were instructed to make three different pre-defined Basic Life Support quality errors (e.g., chest compression too fast) in 7 min. Basic Life Support instructors were randomized to groups of 3-10 participants. Instructors were asked to observe the Basic Life Support skills and to correct performance errors. Primary outcome was the maximum group size at which the percentage of correctly identified participants' errors drops below 80%. RESULTS: Sixty-four instructors participated, eight for each group size. Their average age was 41 ± 9 years and 33% were female, with a median [25th percentile; 75th percentile] teaching experience of 6 [2;11] years. Instructors had taught 3 [1;5] cardiopulmonary resuscitation courses in the year before the study. A logistic binominal regression model showed that the predicted mean percentage of correctly identified participants' errors dropped below 80% for group sizes larger than six. CONCLUSION: This randomized controlled simulation trial reveals decreased ability of instructors to detect Basic Life Support performance errors with increased group size. The maximum group size enabling Basic Life Support instructors to correct more than 80% of errors is six. We therefore recommend a maximum instructor-to-participant ratio of 1:6 for cardiopulmonary resuscitation courses.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".