Four Approaches to Canadian Physician Assistant Education: Does how we teach PAs make a difference? A survey response from Canadian PAs
Bibliographic record
Abstract
The four Canadian Physician Assistant programs each use a unique approach to educate students to achieve the national Physician Assistant (PA) competencies.This descriptive analysis study used an online survey tool to determine if patterns existed from each institute's unique approach to PA Education.This study enquired if the different Canadian PA pedagogical and delivery designs influence the students' entryto-practice comfort or transfer-of-learning to PA students.The survey collected 90 responses from graduates of the four PA programs to identify correlations between their program resources and those used to support their education.When transitioning from didactic year to clinical rotations, most of the responding PA graduates from the University of Manitoba, McMaster University, the Consortium for PA Education (University of Toronto), and the Canadian Armed Forces felt confident in practicing medicine.On average, it took McMaster University graduates nine months to feel comfortable in their role as PAs, eight months for the University of Manitoba graduates, seven months for the University of Toronto graduates and twelve months for Canadian Armed Forces graduates.Notable PAs' transfer-of-learning trends were not noted across the PA programs in Canada despite each program's unique training design.A comparison of five years of national exam results indicated less than a 4% variation from the mean between programs.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".