Transitioning Simulation Education Objectives to Competency-Based Education
Bibliographic record
Abstract
BACKGROUND: Transitioning to competency-based education (CBE) in nursing simulation education requires a systematic approach to curriculum assessment. Information about how to implement CBE in simulation is unclear. METHOD: Using the University's Online Simulation System, 2 experts selected American Association of Colleges of Nursing (AACN) behavioral performance indicators (BPIs) that best matched behavioral outcomes associated with simulation student roles in 5 scenarios. Using a 3-phase process, interrater agreement between 2 experts was resolved through a consensus process (> 89% agreement, 11% least agreed). Next, a third expert validated the first 2 experts' matches (98% agreement, 2% least agreed). RESULTS: One hundred fifty-two expected behavioral outcomes were identified and matched with 39 BPIs. CONCLUSION: A 3-phased approach was used by the faculty to identify and align cognitive simulation objectives to competencies identified in the AACN BPIs. The authors recommend using a systematic approach to validate current simulation activities that are competency-based and identify potential gaps in the curriculum.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".