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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".