Physician associates in general practice: what is their role?
Bibliographic record
Abstract
Physician associates (PAs), previously known as physician assistants, were first employed in the UK in 2003; the role is based on the US role of the same name, which has been established for over 40 years. PAs have now been introduced to many healthcare systems worldwide, including Australia, the Netherlands, Germany, India, and Canada. PAs are mid-level dependent medical professionals who are trained as generalists in the medical model to perform tasks such as obtaining patient medical histories, performing clinical procedures and clinical examinations, diagnosing diseases, and formulating medical management plans. The training courses available within the UK vary in terms of preferred learning approaches: some utilise problem-based learning and others adopt a traditional lecture-based method. However, they are all directed by the outcomes described in the Competence and Curriculum Framework and the list of key conditions that are outlined in the Matrix Specification of Core Clinical Conditions for the Physician Assistant , both of which were initially developed by the Department of Health (DH) in 2006 and revised in 2012.1,2 The Royal College of General Practitioners (RCGP) and the Royal College of Physicians (RCP) jointly led the development of these documents, with input from representatives from universities, employers, patients, junior doctors, and PAs. Following an intensive 24-month postgraduate diploma, all prospective PAs have to undertake a national examination that broadly assesses clinical knowledge and skills. Due to this generalist training PAs are able to offer a flexible skillset that can be utilised in various clinical specialties and can change, as required, over time. To ensure that PAs who move between clinical disciplines …
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".