Integrating interprofessional core competencies through simulation that promotes ethical decisions, patient safety, and cultural diversity
Bibliographic record
Abstract
Introduction: Integrating ethical decisions, patient safety, and cultural diversity through multidisciplinary team-based simulation enhances learning and awareness of interprofessional core competencies.Methods: A simulation scenario was designed to meet educational objectives and create a realistic environment for second-year medical, third-year pharmacy, and third-year nursing students. Students from each of the three disciplines were evenly distributed into groups to participate in a scenario. The simulation-based encounter consisted of a prebrief session, a simulation activity, and an overall debrief session. Course faculty from each discipline facilitated the three mirror-imaged scenarios, observed student behaviors, and operated mid-fidelity simulators. Students’ knowledge and attitudes related to the interprofessional education core competencies (IPE-CC) were evaluated using pre- and post-assessment surveys. Additionally, student feedback was gathered through an opinion survey following the activity.Results: Three-hundred and sixty-one students participated in the simulation activity during the spring semester of the 2021-2022 academic year. A statistical significance was noted with 80% of the pre- and post-assessment survey items. Learner opinion survey results provided favorable feedback as well as suggestions for improvement. The educational objectives were met.Discussion: This simulation activity provides a realistic environment for students to apply the IPE-CC in preparation for their role as an interdisciplinary healthcare team member.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".