Instructional Video of a Standardized Interprofessional Postsimulation Facilitator-guided Debriefing of a Fatality in Plastic Surgery
Bibliographic record
Abstract
Background: Postsimulation facilitator-guided debriefing (PSFGD) is the process of intentional discussion of thoughts, actions, and events that took place during simulation amongst the facilitator(s) and trainees. Despite the significance of delivering high-quality debriefings, there is a lack of evidence-based guidelines. Our study aimed to provide an instructional video demonstration of a PSFGD of a fatality. Methods: Fifty surgical interns participated in a burn simulation scenario in two groups. Group 1 (control, or "no exposure," n = 25) consisted of residents who received oral postsimulation debriefing from an independent faculty member who had no exposure to our instructional video on how to debrief effectively. Group 2 (intervention, or "exposure," n = 25) consisted of interns who were debriefed by the second faculty member who did watch our instructional video before the simulation and learned about "advocacy and inquiry" techniques. The outcome measures were the Debriefing Assessment for Simulation in Healthcare score and the postdebrief multiple-choice question (MCQ) quiz scores to assess debriefers' performance and interns' knowledge consolidation, respectively. Results: < 0.001) compared with the "no exposure" group. Conclusions: Debriefers who followed the methodology as demonstrated in our instructional video were considered more competent, and the residents achieved higher MCQ scores. The quality of the debriefing ensures improved critical thinking and problem-solving skills. Safer practice and better patient outcomes are achieved by developing debriefing programs for educators.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".