Physician assistant/associate retirement intent: seeking the exit ramp
Bibliographic record
Abstract
BACKGROUND: Retirement patterns for American physician assistants/associates (PAs) are in flux as the first substantial cadre trained in the 1970s makes their retirement choices. The growing and aging of the US population is increasing the demand for healthcare services. At the same time, provider retirement can decrease patient access to care, disrupt continuity of care and lead to poorer health outcomes. Knowing PA intentions to retire and the retirement patterns can be useful to health system employers and workforce policymakers. The purpose of this study was to investigate the retirement patterns of PAs within the United States. We investigated their characteristics, career roles, and intent to depart from clinical practice. METHODS: Drawing on the National Commission on Certification of Physician Assistants (NCCPA) 2020 health workforce data (N = 105,699), the associations of demographics (age, gender, US region, and years certified), and practice attributes (specialty and practice setting) of clinically active PAs were assessed with intending to retire in the next five years. Analyses for this national cross-sectional study included descriptive statistics, Chi-square, and Fisher's Exact test, as appropriate. A p-value of 0.05 or less was considered statistically significant for all analyses where a comparison was made. RESULTS: Overall, 5.8% of respondents indicated that they intend to retire within five years. We detected significant differences (all p < 0.001) on intentions to retire by age group, gender, US region, years certified, specialty, and practice setting. Respondents 70 years and older compared to those 60-69 were more likely (66.5% vs. 48.9%), males compared to females (8.8% vs. 4.4%), those who have been certified for more than 21 years compared to 11-20 years (25.6% vs. 4.0%), PAs practicing in family medicine compared to dermatology (7.7% vs. 3.4%) and those in the federal government practice setting compared to rural health clinic (13.6% vs. 9.8%) reported they were more likely to retire in the next five years. CONCLUSIONS: Our study provides a comprehensive snapshot of PA retirement intentions using a robust national dataset. Among the most important factors associated with intent to retire in this study were older age and duration of PA career. Most PAs are remaining clinically active into their seventh decade-suggesting that they are integrated into medical systems that value them and they, in turn, value their role.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".