Physician assistants in Canada: update on health policy initiatives.
Bibliographic record
Abstract
OBJECTIVE: To analyze the health policies related to physician assistants (PAs) and to understand the factors influencing this medical work force movement. QUALITY OF EVIDENCE: This work combines a review of the literature and qualitative information, and it serves as a historical bookmark. The approach was selected when attempts to obtain reports or literature using customary electronic bibliography (PubMed, CINAHL, Google Scholar, EBSCO, and MEDLINE) searches in English and French, from 1970 through 2010, identified only 14 documents (including gray literature) of relevance. Reports, provincial documents, and information from developers of the PA movement supplemented the literature base. MAIN MESSAGE: The historical development of the role of PAs in Canada spans 2 decades. There are now more than 250 PAs, most working in family medicine and emergency medicine. Enabling legislation for PAs has been formalized in Manitoba, and 3 provinces have recognized PAs in various policy statements or initiatives. Three universities and 1 military training centre have enrolled more than 120 students in PA programs. Retired PAs of the Canadian Forces, returning ex-patriot Canadians who had trained as PAs in PA programs in the United States, and American immigrants are working as PAs in Canada. Demonstration projects are under way to better understand the usefulness of PAs in various medical settings. CONCLUSION: For a public health policy enactment of this size and effect, the literature on PAs in Canada is sparse and limited. In spite of this, PA employment is expanding, family medicine practices are using PAs, and there is enabling legislation planned. The result will likely be increased use of PAs. Documentation about PAs, review of their use, and outcomes research are needed to evaluate this new type of clinician in Canadian society.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.039 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".