Translating Interprofessional Education to Practice: The experiences of Physician Assistants within the first years of practice
Bibliographic record
Abstract
In response to the importance and demand of interprofessional collaboration (IPC) in healthcare settings, the World Health Organization acknowledged the need for Interprofessional Education (IPE) when training healthcare professionals. The University of Manitoba (U of M) implemented an IPE course to physician assistant (PA), medical, dentistry, nursing, pharmacy, and rehabilitation sciences students in 2016. Since then, no studies have been conducted to determine whether the IPC program impacted the practice of working PAs. We distributed the Interprofessional Socialization and Valuing Scale (ISVS) survey to graduate PAs from U of M Master of Physician Assistant Studies (MPAS) who did and did not complete a curriculum-integrated IPC course (classes of 2010-2022). The results were compared to ISVS surveys completed by MPAS graduates during their training (classes of 2018-2022) using the Mann-Whitney U-test. From the ISVS part A questions, “I feel comfortable in accepting responsibility delegated to me within a team” had a significant difference between the two groups of practicing PAs (p=0.024). From the part B questions, “I have gained an enhanced awareness of my own role on a team” (p=0.003) and “I feel comfortable being the leader in a team situation” (p=0.002) were significant between the three groups. Although the study did not provide conclusive answers regarding IPE during the MPAS curriculum, practicing PAs who completed IPE were shown to have gained an enhanced awareness of their role on a team. This is consistent with studies demonstrating that IPE can enhance a students’ knowledge of their role within a multidisciplinary team, as well as the roles of other healthcare 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".