Shining a Spotlight on Interprofessional Education & Evaluating an Interprofessional Pediatrics Educational Module Using Simulation
Bibliographic record
Abstract
Interprofessional Education (IPE) occurs when two or more professions learn from, with, and about one another. There is a growing body of research indicating that IPE leads to enhanced professional practice, improved knowledge and skills, more enjoyable learning experiences, and can result in long term cost control from overall improvements in patient safety. Simulation learning, or the reenactment of routine or critical clinical events is now being incorporated into many IPE programs. Program participants work together to perform emergent care skills and scenarios in a controlled environment on high‐fidelity human patient simulators. Interprofessional collaboration and simulation is essential in pediatric care asit contributes to overall patient wellbeing and offers an opportunity to practice the skills used in an acute care incident, events that occur at low frequency in childhood. A research study through the Faculty of Health Sciences, evaluates the introduction of an interprofessional pediatrics educational module amongst nursing and medical students at Queen’s University. This study is part of an innovative pilot project aimed at improving patient welfare and safety through interprofessional health education using patient simulators.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".