A faculty development program for a simulation-based anesthesia curriculum using the Debriefing Assessment for Simulation in Healthcare (DASH)
Bibliographic record
Abstract
Background Simulation-based exercises are well established as a necessary teaching approach in postgraduate anesthesia education. The Future of Medical Education in Canada Postgraduate Project (FMECPP) has recently indicated that faculty development is one of the least developed aspects of simulation in Canada and a key issue in postgraduate education. Accordingly, a simulation program for anesthesia residents was designed to promote continuous faculty development through the creation and implementation of simulation scenarios. Program Description The program provides monthly simulation sessions and debriefings to residents which are faculty-lead and based on core anesthesia curriculum. The simulation committee who creates and delivers the scenarios is comprised of four anesthesia faculty members and one anesthesia resident. Three times a year, the simulation committee meets and each instructor delivers the scenario they have prepared to be incorporated into the program. Two instructors deliver the scenario and remaining members act as participants. After each scenario, the scenario instructor will guide a debriefing session which is assessed using the debriefing assessment for simulation in healthcare (DASH) tool by the faculty who acted as participants. Conclusions With the DASH tool being used to direct improvements in the debriefing process, this program allows the simulation committee to practice their debriefing skills while correcting their areas of weakness. It also provides the opportunity to fine-tune or change the scenarios to better work for residents, contributing to improved teaching performance for faculty and better learning outcomes for residents. Continuous faculty development of student-centered learning is also achieved as residents complete a student DASH form evaluating the quality of the debriefing sessions throughout the program.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".