Interprofessional simulation to promote teamwork and communication between nursing and respiratory therapy students: A mixed-method research study
Bibliographic record
Abstract
BACKGROUND: Collaboration and teamwork are vitally important for safe patient care. Experiential learning through interprofessional simulation helps prepare students for the expansive requirements of today's complex healthcare environment. PURPOSE: To develop and evaluate an interprofessional simulation educational activity to promote teamwork and communication between respiratory therapy and nursing students. DESIGN: A mixed method design employing surveys, observation, and focus groups with educators and students was used. Thirty-six students from two institutions in Western Canada participated in this study. Data was analyzed using descriptive statistics and content analysis. RESULTS: Baseline assessment revealed students were most familiar and comfortable with team functioning and communication interprofessional competencies, familiar but uncomfortable with collaborative leadership, conflict resolution, patient-centered care, and role clarification competencies. Correlation between communication and teamwork and collaborative leadership suggests these competencies play an important role in students' ability to enact more complex skills, such as conflict resolution competency. Overall, participants were highly satisfied and shared invaluable insights for improving this simulation experience in the future. CONCLUSION: This evaluation study demonstrated feasibility of interprofessional simulation and its potential to enhance acquisition of interprofessional competencies. A future study will incorporate additional disciplines, such as medicine and pharmacy, applying a controlled evaluation design.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".