Impact of Video-Based Error Correction Learning for Cardiopulmonary Resuscitation Training: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Video-based error correction (VBEC) in medical education could offer immediate feedback, promote enhanced learning retention, and foster reflective practice. However, its application in cardiopulmonary resuscitation (CPR) training has not been investigated. Objective: The objective of this study is to assess whether the VBEC procedure could improve the training performance of CPR among anesthesiology residents. Methods: A quasi-experimental study was conducted among anesthesiology residents between December 2022 and April 2023. Primary outcomes included a posttraining knowledge test and practical assessment scores. Secondary outcomes included the number of residents who correctly conducted CPR at each step, the rate of common mistakes during the CPR process, and the self-assessment results. A total of 80 anesthesiology residents were divided into a VBEC group (n=40) and a control group (n=40). The VBEC group underwent a 15-minute VBEC CPR training, whereas the control group underwent a 15-minute video-prompting CPR training. Results: The posttraining knowledge test score of the VBEC group was significantly higher than that of the control group (73, SD 10.5 vs 65.1, SD 11.4; P=.002). The residents in the VBEC group had lower error rates in "failure to anticipate the next move" (n=3, 7.5% vs n=13, 32.5%; P=.01) and "failure to debrief or problem solve after the code" (n=2, 5% vs n=11, 27.5%; P=.01), as well as better performance in the "secure own safety" step (n=34, 85% vs n=18, 45%; P<.001) than those in the control group. The VBEC group showed significantly higher confidence in CPR than the control group (n=?, 62.5% vs n=?, 35%; P=.03). Conclusions: VBEC may be a promising strategy compared to video prompting for CPR training among anesthesiology residents.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".