Innovations in applied health: Evaluating a simulation-enhanced, interprofessional curriculum
Bibliographic record
Abstract
BACKGROUND: In response to current trends in healthcare education, teachers at the Michener Institute for Applied Health Sciences implemented a New Curriculum Model (NCM) in 2006, building a curriculum to better transition students from didactic to clinical education. Through the implementation of interprofessional education and simulated clinical scenarios, educators created a setting to develop, contextualize and apply students' skills before entry to the clinical environment. AIMS: In this pilot study, researchers assessed the impact of the NCM intervention on student preparedness for clinical practicum. METHODS: A mixed-methods evaluation was conducted, collecting survey assessments and qualitative focus group feedback from clinical educators and students. RESULTS: Clinical educators identified Michener NCM students to be significantly better prepared for clinical practicum when compared to previous cohorts (p < 0.05%). Students also noted significant improvements as implementation issues were resolved from years one to two of the NCM. CONCLUSIONS: The infusion of simulation and interprofessional education into Michener's applied health curricula resulted in a significant improvement in clinical preparedness. The Michener NCM bridged the gap previously separating didactic education and clinical practice, transitioning applied health students from trained technicians to more complete health care professionals.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".