Bibliographic record
Abstract
Background/rationale: Simulation-Augmented Education and Training (SAET) is an effective educational intervention aiding to prepare health professionals for their practice.SAET contributions range from preparing novices to be effective in a clinical setting to ensuring competence of seasoned professionals performing low-frequency, high-stakes skills.SAET is a complex intervention typically delivered by a team of educators to a team of learners.An algorithm consisting of pre-briefing, briefing, simulation experience, and de-briefing (PBSD) may help to reduce complexities of SAET, thus making it most effective.PBSD ensures proper communication of the learning objectives across the team of educators and the learners, linking these objectives to the specific simulation exercises and ensuring that they are adequately addressed during a well-constructed debriefing.This interactive workshop will demonstrate skills and processes that can be employed to conduct a proper PBSD.Objectives: Upon completion of this workshop, the participants will be able to (1) understand and apply the principles of applying the PBSD algorithm; (2) link learning objectives, debriefing methods, and assessment strategies to all parts of the algorithm; (3) develop specific simulation exercises utilizing institutional (Clinical learning and Simulation Centrespecific) templates; and (4) learn and apply appropriate debriefing strategies.Teaching Methods: Three teaching methodologies will be employed: 1. video demonstrations of suboptimal and proper debriefing strategies (20 minutes); 2. didactic lecture outlining components of PBSD (20 minutes); 3. interactive co-development of PBSD for a selected group of simulation scenarios (40 minutes); and 4. debriefing and summary (10 minutes).
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.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".