Randomised, controlled study investigating the optimal instructor: student ratios for teaching suturing skills
Bibliographic record
Abstract
INTRODUCTION: Recently, there has been a shift away from practising procedures on patients for the first time and towards bench model teaching of clinical skills to undergraduate medical students. However, guidelines for the most effective instructor : student ratio for technical skills training are unclear. This has important implications for staffing laboratory based teaching sessions. The purpose of this study was to assess the optimal ratio of teachers to learners during the teaching of a simulated wound closure. METHODS: A total of 108 undergraduate medical students participated in a 1-hour course on wound closure. They were randomised to 3 groups, each with a different instructor:student ratio (Group A: 6-12; Group B: 3-12; Group C: 1-12). Students were evaluated on a pre-test, an immediate post-test and a delayed retention test using an objective, computer-based technical skills assessment method. Collectively termed the "economy of movements", the total time taken to complete the task and the number of movements executed were the primary outcome measures. RESULTS: Improvements in the economy of movements were the same for Groups A and B and were better than in Group C (P < 0.005). DISCUSSION: The optimal instructor:student ratio was 1 instructor for 4 students. Higher ratios of instructors to students resulted in no improvements in learning, and lower ratios of instructors to students resulted in significantly less learning. These findings are in keeping with current motor learning theories.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".