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 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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".