Promoting Excellence and Reflective Learning in Simulation (PEARLS)
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: We describe an integrated conceptual framework for a blended approach to debriefing called PEARLS [Promoting Excellence And Reflective Learning in Simulation]. We provide a rationale for scripted debriefing and introduce a PEARLS debriefing tool designed to facilitate implementation of the new framework. The PEARLS framework integrates 3 common educational strategies used during debriefing, namely, (1) learner self-assessment, (2) facilitating focused discussion, and (3) providing information in the form of directive feedback and/or teaching. The PEARLS debriefing tool incorporates scripted language to guide the debriefing, depending on the strategy chosen. The PEARLS framework and debriefing script fill a need for many health care educators learning to facilitate debriefings in simulation-based education. The PEARLS offers a structured framework adaptable for debriefing simulations with a variety in goals, including clinical decision making, improving technical skills, teamwork training, and interprofessional collaboration.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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