Characteristics of family medicine physician associates/assistants in the United States
Bibliographic record
Abstract
BACKGROUND: The family medicine (FM) workforce in the United States (U.S.) includes physicians, physician associates/assistants (PAs), and nurse practitioners (NPs). These family medicine practitioners are patients' first point of contact, providing preventative, continuous healthcare, care coordination, and patient-centered care. Yet, despite their importance in the healthcare system, the shortage of FM practitioners persists. Considering the general medical education of PAs and limited research on their roles in FM, this research aims to understand the employment characteristics of this professional group. METHODS: This cross-sectional observational research utilized a 2023 national dataset from the National Commission on Certification of PAs (NCCPA). We used standard descriptive and bivariate analyses to summarize the demographic and professional attributes of PAs in FM (n = 20,940) versus those in all other specialties (n = 106,001). RESULTS: In 2023, 16.5% of PAs indicated working in FM. PAs in FM had a median age of 41, and 68.8% self-identified as female. PAs in FM versus those in other medical specialties (all p < 0.001) were more likely to self-identify as Black/African American (3.8% versus 3.4%) and Hispanic/Latino(a/x) (9.3% versus 6.6%). A higher ratio of PAs in FM indicated practicing in rural/isolated settings (15.3% versus 5.7%) and reported using another language other than English to communicate with patients (26.4% versus 21.7%; all p < 0.001) than their colleagues in other medical specialties. FM PAs were more likely to indicate working in office-based private settings, community health centers, rural health clinics, and public/community health clinics than PAs in all other specialties. Compared to their colleagues in other specialties, a higher percentage of PAs in FM indicated providing telemedicine services (66.1% versus 38.1%; p < 0.001). Family medicine PAs versus those in other medical disciplines reported earning $10,000 less (annual income). A slightly lower proportion of PAs in FM reported being satisfied with their current employment and were more likely to experience symptoms of burnout compared to PAs in other medical specialties. CONCLUSIONS: This study reinforces the critical role of PAs in FM in mitigating the healthcare access crisis, particularly in underresourced and rural areas. A better understanding of factors associated with PAs pursuing and sustaining practice in FM could alleviate some of the burden of the projected healthcare provider shortage in primary care.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".