Providing care in underresourced areas: contribution of the physician assistant/associate workforce
Bibliographic record
Abstract
BACKGROUND: Prior studies suggest that physician assistants/associates (PAs) are more likely than physicians to work in underresourced areas. However, data characterizing the current PA workforce in health professional shortage areas (HPSAs) and medically underserved areas (MUAs) are lacking. METHODS: We analyzed the 2022 cross-sectional dataset from a comprehensive national database to examine the demographic and practice characteristics of PAs working in HPSAs/MUAs compared to those in other settings. Analyses included descriptive and bivariate statistics, along with multivariate logistic regression. RESULTS: Nearly 23% of PAs reported practicing in HPSAs/MUAs. Among PAs in HPSAs/MUAs, over a third (34.6%) work in primary care settings, 33.3% identify as men, 15.6% reside in rural/isolated areas, and 14.0% are from an underrepresented in medicine (URiM) background. Factors associated with higher odds of practicing in a HPSA/MUA included residing in rural/isolated settings, URiM background, and speaking a language other than English with patients. CONCLUSIONS: As the PA profession grows, knowledge of these attributes may help inform efforts to expand PA workforce contributions to address provider shortages.
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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 | 0.010 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".