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 a critical component in the process of learning through healthcare simulation. This critical review examines the timing, facilitation, conversational structures, and process elements used in healthcare simulation debriefing. Debriefing occurs either after (postevent) or during (within-event) the simulation. The debriefing conversation can be guided by either a facilitator (facilitator-guided) or the simulation participants themselves (self-guided). Postevent facilitator-guided debriefing may incorporate several conversational structures. These conversational structures break the debriefing discussion into a series of 3 or more phases to help organize the debriefing and ensure the conversation proceeds in an orderly manner. Debriefing process elements are an array of techniques to optimize reflective experience and maximize the impact of debriefing. These are divided here into the following 3 categories: essential elements, conversational techniques/educational strategies, and debriefing adjuncts. This review provides both novice and advanced simulation educators with an overview of various methods of conducting healthcare simulation debriefing. Future research will investigate which debriefing methods are best for which contexts and for whom, and also explore how lessons from simulation debriefing translate to debriefing in clinical practice.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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