Physician assistants/associates in psychiatry: a workforce analysis
Bibliographic record
Abstract
BACKGROUND: Physician assistants/associates (PAs) provide services in diverse medical specialties globally, including psychiatry. While health professionals in psychiatry have been described for many years, little is known about PAs practicing in this discipline. METHODS: We describe US PAs practicing in psychiatry using robust national data from the National Commission on Certification of Physician Assistants (NCCPA). Analyses included descriptive and inferential statistics comparing PAs in psychiatry to PAs in all other medical and surgical specialties. RESULTS: The percentage of PAs practicing in psychiatry has increased from 1.1% (n = 630) in 2013 to 2.0% (n = 2 262) in 2021. PAs in psychiatry differed from PAs practicing in all other specialties in the following: they identified predominately as female (71.4% vs. 69.1%; p = 0.016), were more racially diverse (Asian [6.6% vs. 6.0%], Black/African American [5.5% vs. 3.4%], multi-race [2.8% vs. 2.1%], and other races [Native Hawaiian/Pacific Islander, American Indian/Alaska Native, or other; 3.7% vs. 3.6%]; p < 0.001), and resided in the South (43.8% vs. 34.1%; p < 0.001). PAs in psychiatry vs. all other specialties were more likely to work in office-based private practice settings (41.6% vs. 37.3%; p < 0.001) and nearly twice as likely to provide telemedicine services for their patients (62.7% vs. 32.9%; p < 0.001). While one-third (31.9%) of PAs in psychiatry experienced one or more burnout symptoms, and 8.1% considered changing their current position, the vast majority of PAs in psychiatry (86.0%) were satisfied with their position. CONCLUSIONS: Understanding the attributes of PAs in psychiatry is essential in medical labor supply and demand research. Our findings suggest that the number of PAs working in psychiatry is steadily increasing. These PAs were predominantly female, exhibited greater racial diversity, and were primarily located in the South and Midwest regions of the US. A striking difference was that PAs in psychiatry were almost twice as likely to provide telemedicine services for their patients. Although nearly a third of PAs in psychiatry acknowledged having one or more symptoms of burnout, few were considering changing their employment, and the vast majority reported high job satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".