A longitudinal study on the impact of simulation on positive deviance through speaking up
Bibliographic record
Abstract
Background Students reported positive learning outcomes during a simulation study addressing compliance and speaking up. Purpose Investigate if the impacts of the simulation had a lasting effect on participants after moving into practice. Method Semi-structured interviews focusing on memory of the study, psychological impacts, educational impacts, professional impacts, and experiences in practice were conducted with Advanced Care Paramedics (3) and Respiratory Therapists (7) between 19 and 24 months after the original study. Discussion Participants indicated the simulation helped them develop the skill and confidence to speak up, preparing them to speak up in practice. Primary findings included: (i) the importance of experience for speaking up, (ii) the benefit of high-impact simulation, and (iii) the importance of simulation training. Conclusions Simulation for speaking up should occur early. Conducting high-impact simulations for speaking up is a practical and actionable intervention that appears to enhance confidence, ability, and likelihood of speaking up in practice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".