Interprofessional Simulation for Nursing and Paramedicine Students
Bibliographic record
Abstract
BACKGROUND: Interprofessional simulations are becoming an important aspect of learning for nursing students. Still, the execution of these types of simulations can prove challenging. PROBLEM: Educational institutions often struggle to access faculties from different health care fields for interprofessional simulations. There is limited literature on operationalizing and implementing interprofessional simulations related to scenarios in the community, which makes creating these simulations challenging. APPROACH: Nursing and paramedicine educators from a university and a community college collaborated on a simulation centered on the management of immunization anaphylaxis in the community. OUTCOMES: Feedback from facilitators and students was positive. Both disciplines agreed that collaborative learning significantly enhanced role clarification, team functioning, conflict resolution, collaboration, and communication skills with team members. Challenges included coordinating schedules, space, and training of facilitators. CONCLUSIONS: Collaborating with other educational institutions to establish an interprofessional simulation can be complex, but the benefits significantly outweighed the challenges.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".