Integrating Virtual Simulation with Course Curriculum to Improve Patient Advocacy through Speaking Up
Bibliographic record
Abstract
Abstract Purpose Healthcare teams consist of interdisciplinary groups of health professionals that tend to be hierarchically structured. Often it is necessary to challenge authority through speaking up, however, within hierarchies’ individuals tend to demonstrate obedience to authority. Speaking up is an essential skill for preventing patient harm that must be developed. Virtual Simulation integrated with curriculum using Kolb’s Learning Cycle is a promising avenue for improving interprofessional collaboration. Methods An experimental design was used to determine if a gamified VS along with instruction on patient-advocacy would increase the rate of speaking up in Respiratory Therapy students (n=34) during in-person simulation delivered one month after the classroom instruction. The in-person simulation required students to challenge a senior anesthesiologist to prevent patient harm. Effects of personality and individual differences were also examined. Results The VS resulted in speaking up at a higher rate than in the control condition (p=0.04) and used CUS more often (p<0.001). No individual differences or personality measures were predictive of speaking up. Conclusion The findings from the present study support the integration of VS with course curriculum on patient-advocacy, showing improved performance during in-person simulation one month after course delivery. Longitudinal investigation is necessary to determine if the improved performance is indicative of increased likelihood of speaking up in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".