Fostering Undergraduate Medicine, Nursing, and Pharmacy Students’ Readiness for Interprofessional Learning Using High Fidelity Simulation
Bibliographic record
Abstract
Background Interprofessional education is directly linked to high-quality patient care, however, it remains unclear whether senior undergraduate medicine, nursing, and pharmacy students are ready for interprofessional education using high fidelity human patient simulators. Purpose The purpose of this study was to explore student's readiness for interprofessional learning and determine whether participation in high fidelity interprofessional education resulted in higher levels of readiness for interprofessional learning. Methods An interventional program starting with a pre-test before the program and a post-test after the program ends were designed with 24 students. The students were assigned to seven interprofessional teams. Each team participated in a high fidelity interprofessional education module designed to teach the clinical management of an adult patient experiencing acute anaphylaxis. The Readiness for Interprofessional Learning Scale (RIPLS) was used as the pre and post-test instrument. Results Prior to participation, students reported a high level of readiness for interprofessional learning, but that readiness significantly improved after participation, including more positive attitudes towards teamwork, enhanced communication skills, and improved respect and trust for team members. Conclusions The findings from this study show a higher level of readiness for high fidelity interprofessional learning using human patient simulators among senior undergraduate medicine, nursing, and pharmacy students. These findings support the integration of high fidelity interprofessional education into undergraduate medicine, nursing, and pharmacy undergraduate education programs.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".