Personalized Oral Debriefing Versus Standardized Multimedia Instruction After Patient Crisis Simulation
Bibliographic record
Abstract
BACKGROUND: Simulation experience alone without debriefing is insufficient for learning. Standardized multimedia instruction has been shown to be useful in teaching surgical skills but has not been evaluated for use as an adjunct in crisis management training. Our primary purpose in this study was to determine whether standardized computer-based multimedia instruction is effective for learning, and whether the learning is retained 5 wk later. Our secondary purpose was to compare multimedia instruction to personalized video-assisted oral debriefing with an expert. METHODS: Thirty anesthesia residents were recruited to manage three different simulated resuscitation scenarios using a high-fidelity patient simulator. After the first scenario, subjects were randomized to either a computer-based multimedia tutorial or a personal debriefing of their performance with an expert and videotape review. After their respective teaching, subjects managed a similar posttest resuscitation scenario and a third retention test scenario 5 wk later. Performances were independently rated by two blinded expert assessors using a previously validated assessment system. RESULTS: Posttest (12.22 +/- 2.19, P = 0.009) and retention (12.80 +/- 1.77, P < 0.001) performances of nontechnical skills were significantly improved in the standardized multimedia instruction group compared with pretest (10.27 +/- 2.10). There were no significant differences in improvement between the two methods of instruction. CONCLUSION: Computer-based multimedia instruction is an effective method of teaching nontechnical skills in simulated crisis scenarios and may be as effective as personalized oral debriefing. Multimedia may be a valuable adjunct to centers when debriefing expertise is not available.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".