Faculty Development for Simulation Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
STATEMENT: Debriefing is widely recognized as a critically important element of simulation-based education. Simulation educators obtain and/or seek debriefing training from various sources, including workshops at conferences, simulation educator courses, formal fellowships in debriefings, or through advanced degrees. Although there are many options available for debriefing training, little is known about how faculty development opportunities should be structured to maintain and enhance the quality of debriefing within simulation programs. In this article, we discuss 5 key issues to help shape the future of debriefing training for simulation educators, specifically the following: (1) Are we teaching the appropriate debriefing methods? (2) Are we using the appropriate methods to teach debriefing skills? (3) How can we best assess debriefing effectiveness? (4) How can peer feedback of debriefing be used to improve debriefing quality within programs? (5) How can we individualize debriefing training opportunities to the learning needs of our 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it