Economic Analysis of Physician Assistants in Ontario: Literature Review and Feasibility Study
Bibliographic record
Abstract
We conducted a literature review of studies on Physician Assistants working in a variety of settings and found few evaluation studies on the costs and/or effectiveness of Physician Assistants in primary care practices, Emergency Departments and in hospital settings other than Emergency Departments. The existing literature is limited because of the non-Canadian settings in which most studies have been performed and because of the non-experimental study designs, which are subject to potential bias. In addition, the research questions that have been addressed have tended to ignore what would appear to be the most important comparison: that between Physician Assistants and other non-physician providers such as Nurse Practitioners. The evidence we found on the cost-effectiveness of PAs is anecdotal and difficult to translate in the Ontario context. We conclude that it is difficult to make use of the existing literature. We recommend that MOHLTC consider options for funding a randomized control trial that might involve several trial arms in the particular sectors of relevance to the PA program, for example: physician only; physician and PA; physician and NP; and physician, NP and PA. The purpose of this would be to explore the difference in costs and effects on the different service modalities. This would also provide sufficient information to support modelling the short-run effects that could be expected from allocating the same amount of resources to the different service modalities as well as the implications for physician resources planning.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".