Debriefing for technology‐enhanced simulation: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Debriefing is a common feature of technology-enhanced simulation (TES) education. However, evidence for its effectiveness remains unclear. We sought to characterise how debriefing is reported in the TES literature, identify debriefing features that are associated with improved outcomes, and evaluate the effectiveness of debriefing when combined with TES. METHODS: We systematically searched databases, including MEDLINE, EMBASE and Scopus, and reviewed previous bibliographies for original comparative studies investigating the use of TES with debriefing in training health care providers. Reviewers, in duplicate, evaluated study quality and abstracted information on instructional design, debriefing and outcomes. Effect sizes (ES) were pooled using random-effects meta-analysis. RESULTS: From 10 903 potentially eligible studies, we identified 177 studies (11 511 learners) that employed debriefing as part of TES. Key characteristics of debriefing (e.g. duration, educator presence and characteristics, content, structure/method, timing, use of video) were usually incompletely reported. A meta-analysis of four studies demonstrated that video-assisted debriefing has negligible and non-significant effects for time skills (ES = 0.10) compared with non-video-assisted debriefing. Meta-analysis demonstrated non-significant effects in favour of expert modelling with short debriefing in comparison with long debriefing (ES range = 0.21-0.74). Among studies comparing terminal with concurrent debriefing, results were variable depending on outcome measures and the context of training (e.g. medical resuscitation versus technical skills). Eight additional studies revealed insight into the roles of other debriefing-related factors (e.g. multimedia debriefing, learner-led debriefing, debriefing duration, content of debriefing). Among studies that compared simulation plus debriefing with no intervention, pooled ESs were favourable for all outcomes (ES range = 0.28-2.16). CONCLUSIONS: Limited evidence suggests that video-assisted debriefing yields outcomes similar to those of non-video-assisted debriefing. Other debriefing design features show mixed or non-significant results. As debriefing characteristics are usually incompletely reported, future debriefing research should describe all the key debriefing characteristics along with their associated descriptors.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".