Develop, Sustain, and Evaluate the Training of Simulation Educators
Bibliographic record
Abstract
BACKGROUND: Developing the competency of simulation educators is critical for optimizing learner outcomes. Yet guidelines on how to sustain received simulation training and evaluate training programs are limited. PURPOSE: To examine the impact of a professional development workshop (PDW) aimed at individuals responsible for developing, sustaining, and evaluating simulation educator training programs. METHODS: A longitudinal exploratory design was used, guided by the New World Kirkpatrick Model. RESULTS: Seventy-seven participants from 6 countries and 5 professions participated at the outset of the study, with 56% completing the entire study at the 6-month mark. Significant changes in knowledge, confidence, and commitment were observed from pre-to-post PDW. Themes of personal capacity, supportive mechanisms, and embracing accountability were identified as facilitators to develop/evaluate training programs, whereas their absence acted as barriers. CONCLUSIONS: Develop a training program evaluation plan from the outset. Sustain the training of simulation leaders and educators through intentional processes that support, reinforce, monitor, and reward efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".