A Qualitative Evaluation of Medical Students' Perspectives Regarding Collaboration with Physician Assistants
Bibliographic record
Abstract
INTRODUCTION: Physician assistant (PA) is a burgeoning profession in Canada, with several accredited training programs. Because the scope of practice for PAs in Ontario, as delineated by the province, stipulates that all tasks they perform must be delegated by a supervising physician, it is expected that medical students will increasingly encounter and work alongside PAs in clinical environments. There has been a paucity of research to date investigating how medical students experience this professional relationship. This current study aimed to investigate the attitudes and perspectives that medical students have about working with PAs. METHODS: Medical students from the University of Toronto (n = 11) in various stages of training participated in 3 focus groups. The focus groups used a semi-structured interview guide to explore medical students' general opinions of the profession, their understanding of the interprofessional relationship, and their experiences working with PAs. Qualitative methods with a phenomenological underpinning were used to analyze the focus groups. RESULTS: The findings show that medical students have observed or collaborated with PAs in clinical environments but are generally unaware of the profession's scope of practice and responsibilities. Medical students also viewed PAs as beneficial to patient care and expressed a desire to discover more about the profession through formal education. DISCUSSION: This call for interprofessional education should be heeded by medical faculty to better prepare medical students for future collaboration with PAs.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".