Health workforce: a case for physician assistants?
Bibliographic record
Abstract
Health workforce shortages are a global phenomenon. Dealing with these shortages requires a multi dimensional strategy that some developed countries have recognised may need to include the introduction of new health professionals. These professionals supplement the work of medical practitioners in dealing with changing population health needs. One such complementary practitioner, the physician assistant, has made significant contributions to the United States’ health system for over forty years. In the United States, physician assistants have proven to be an efficient and cost effective means to deliver health care and demand for their services is growing. Other developed and developing nations have either adapted the United States physician assistant model to suit their health system or have shown interest in the model. In Australia, debate is still underway concerning the merits of alternative practitioners. Primarily, this debate centres on whether these practitioners constitute a threat to the quality and safety of health care. This paper outlines the development of the physician assistant model in the United States, Britain and Canada and considers the possible application of the model to the Australian health system. It concludes there is potential to adapt this model to suit the Australian health system so that quality of care and safety in the delivery of services is not compromised.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.028 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".