A Curricular Review of Radiology Education in a Master of Physician Assistant Studies Program
Bibliographic record
Abstract
INTRODUCTION: Physician assistants/associates (PAs) are expected to be competent in ordering and interpreting diagnostic imaging. However, there are no further details outlining the educational expectations of PAs as it relates to radiology upon graduation. This can result in significant variability in the radiology curricula that PA students are taught and, consequently, hinder PAs' ability to work within their full scope of practice. Therefore, the purpose of this study was to map the radiology curriculum in a Master of Physician Assistant Studies (MPAS) program to elucidate radiological educational training before graduation. METHODS: Quantitative curricular mapping was used to assess the 2021 to 2022 MPAS program for radiological involvement. Relevant course and session objectives related to radiology education were identified. In addition, educational learning material was reviewed for diagnostic imaging content. RESULTS: Formal radiological training was observed in 8 of 27 courses in the preclinical curriculum, with 4.35% of the total session objectives directed to radiological education. This formal exposure comprises 18.9 hours (1.71%) of curricular time. Informal diagnostic imaging exposure increased radiology education to approximately 29.5 hours (2.67%) of curricular time. One course (Diagnostic Imaging) focuses exclusively on radiology teaching and accounts for approximately 50% of the total radiologic teaching. X-ray ordering and interpretation received the greatest emphasis throughout the curriculum, while ultrasound received the least attention. DISCUSSION: Further integration of formal radiological education into PA programs should be considered with specific attention directed toward point-of-care ultrasound exposure and ordering/interpretation skills.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".