Characteristics of Physician Assistants/Associates in the Uniformed Services
Bibliographic record
Abstract
OBJECTIVE: Physician assistants/associates (PAs) serve as commissioned medical officers in the uniformed services, supporting readiness, global health operations, and domestic response. Despite their critical contributions, limited data exist to inform strategic workforce planning, recruiting, and retention efforts. MATERIALS AND METHODS: A cross-sectional quantitative analysis was conducted using a 2023 national workforce dataset. We compared the demographic and employment characteristics of PAs on active duty with those not on active duty (N=12,146), using descriptive statistics and bivariate analysis (Pearson chi-square tests for categorical variables and Mann-Whitney tests for continuous variables). RESULTS: The PA Professional Profile data identified 12,146 PAs who reported their armed forces status, with 2,508 (20.6%) indicating they were on active duty, although 9,639 (79.4%) were inactive (retired and veterans). As of 2023, 2,508 active duty PAs served in the Army (52.0%), Air Force (25.8%), Navy/Marines (20.1%), USPHS/NOAA (5.9%), and Coast Guard (3.0%). In terms of demographics, PAs in active duty report a median age of 41, with 29.9% female and 11.6% indicating Hispanic/Latinx ethnicity. Compared to PAs not on active duty, PAs on active duty were more likely to complete a postgraduate fellowship/residency (17.6% vs. 13.9%). Over half of active duty PAs participate in telemedicine. More than a third (37.2%) of active duty PAs report symptoms of burnout, compared to 30.1% of PAs not on active duty. CONCLUSIONS: PAs occupy unique roles in the U.S. government's uniformed services. Federal agencies should enhance PA retention initiatives by addressing workload demands, aligning compensation with responsibilities, expanding leadership and fellowship opportunities, and incorporating flexible, nonclinical career pathways into retention packets. Such strategies are essential to sustain a resilient, skilled, mission-ready uniformed PA workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".