The effect of scripted debriefing in resuscitation training: A scoping review
Bibliographic record
Abstract
Objectives: To evaluate the effectiveness of scripted debriefing relative to no use of script during debriefing in resuscitation training. Methods: This scoping review was undertaken as part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR) and based on the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) extension for scoping review. MEDLINE, EMBASE, and SCOPUS were searched from inception to January 2024. We included all published studies comparing scripted debriefing vs non-scripted debriefing evaluating patient outcomes, behaviour change of learners, learning outcomes for learners and cognitive load and teaching quality for instructors. Results: Our initial literature search identified 1238 citations. After removing 552 duplicates, reviewing the titles and abstracts of the remaining 686 articles yielded 11 for full-text review. Of these, six articles were selected for inclusion in the final analysis. The six studies described debriefing scripts varying in content, framework, scripted language and the integration of objective data. Scripted debriefing improved CPR performance, team leadership skills and knowledge acquisition, but showed no difference in teamwork performance compared to non-scripted debriefing. Scripted debriefing also improved debriefing quality and decreased cognitive load of the instructor during resuscitation training. Conclusion: The use of a debriefing script during resuscitation education can improve CPR performance, team leader performance, knowledge acquisition and reduce the debriefer's cognitive load. Future research should explore how debriefing scripts can be designed to optimize learning outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".