Physician assistant/associate career flexibility: factors associated with specialty transitions
Bibliographic record
Abstract
BACKGROUND: Physician assistants/associates (PAs), due to their broad medical education and certification, have the flexibility to change specialties throughout their careers. Prior studies suggest that between half and three-quarters exercise this option at some point in their career, and a third do so within the first decade. However, more research is needed to understand the factors associated with PAs changing vs. remaining in the same specialty throughout their professional journey. METHODS: A cross-sectional analysis using a 2023 national dataset was conducted to examine specialty transitions throughout PAs' careers. We analyzed responses (N = 121,947 [81.3% response rate]) to a question asking how many times PAs changed specialties throughout their careers. Our investigation included specialty changes overall and separately within career-stage subgroups. We also explored demographics and practice attributes associated with specialty change patterns. RESULTS: Most PAs change specialties at least once during their careers, with some making this transition as many as six or more times. The highest proportion of change occurs in the first, followed by the second decade of practice. In the first 10 years, 40.3% of PAs changed specialties. In multivariate analysis, duration of practice was the strongest factor associated with increased odds of transitioning. PAs practicing for 31 or more years had over four-fold higher odds of changing specialties than PAs practicing for 10 or fewer years. Other factors strongly linked to specialty changes included working in critical care medicine or psychiatry, holding two or more clinical positions, practicing in urgent care, being an independent contractor, female gender, serving in the military, and possessing a doctorate. The pattern of significant PA characteristics linked with specialty changes was largely consistent when comparing the overall cross-sectional analysis with separate subgroups of PAs within different career stages. CONCLUSION: PAs exhibit a high degree of career flexibility when it comes to changing specialties. Our research demonstrates that by the late-career stage, three-fourths of PAs have transitioned between at least two disciplines; however, most changes occurred in the first decade of practice. This study further substantiates the PA profession's attributes, highlighting its unique characteristics, such as its adaptability and capacity for career flexibility.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".