Guiding, Intermediating, Facilitating, and Teaching (GIFT): A Conceptual Framework for Simulation Educator Roles in Healthcare Debriefing.
Bibliographic record
Abstract
INTRODUCTION: Use of frameworks for simulation debriefing represents best practice, although available frameworks provide only general guidance. Debriefers may experience difficulties implementing broad recommendations, especially in challenging debriefing situations that require more specific strategies. This study describes how debriefers approach challenges in postsimulation debriefing. METHODS: Ten experienced simulation educators participated in 3 simulated debriefings. Think-aloud interviews before and after the simulations were used to explore roles that debriefers adopted and the associated strategies they used to achieve specific goals. All data were audio recorded and transcribed, and a constructivist grounded theory approach was used for analysis. RESULTS: 4 roles in debriefing were identified: guiding, (inter)mediating, facilitating integration, and teaching. Each role was associated with specific goals and strategies that were adopted to achieve these goals. The goal of creating and maintaining a psychologically safe learning environment was common across all roles. These findings were conceptualized as the GIFT debriefing framework. CONCLUSIONS: Our findings highlight the multiple roles debriefers play and how these roles are enacted in postsimulation debriefing. These results may inform future professional development and mentorship programs for debriefing in both simulation-based education and healthcare settings.
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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 | 0.057 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".