Physician Assistants in Clinical Endocrinology: Characteristics and Demographics
Bibliographic record
Abstract
Objective Physician assistants (PAs) are employed in endocrinology, but little is known about their roles and activities. The study aimed to assess PAs' employment characteristics in endocrinology compared to those in all other specialties. Methods This descriptive observational study used the 2022 National Commission on Certification of PAs dataset. The study includes 117 748 board-certified PAs who indicated a clinical specialty in 2022. The characteristics of PAs in endocrinology were examined using descriptive statistics, including counts and percentages for categorical variables; means (with standard deviations), and medians (with interquartile ranges) for continuous variables. Bivariate analyses (ꭙ 2 and Mann–Whitney U tests) were used to determine statistical differences between PAs practicing in endocrinology versus PAs in all other specialties. Results This study found that as of 2022, 685 PAs reported practicing in endocrinology. PAs in endocrinology, compared to PAs in all other specialties (all P < .001), were more likely to identify as female (82.0% vs 69.6%), work in an office-based private practice (61.3% vs 37.0%), and participate in telemedicine (70.8% vs 40.1%). Conversely, PAs in endocrinology were less likely to work in a secondary position, saw slightly fewer patients weekly, and earned $10,000 less yearly than their PA colleagues in all other specialties. Conclusion Examining the PA endocrinology workforce is essential due to the shortage of endocrinologists and the increased prevalence of diabetes as the U.S. population ages. Understanding where PAs in endocrinology are employed and their attributes could assist efforts in specialty modeling to address supply and demand projections.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".