Evaluating the need for physician assistants in sport and exercise medicine
Bibliographic record
Abstract
INTRODUCTION: Injuries in sport, are a common occurrence. When these injuries occur, it can affect the patient outside of sport. Therefore, the 19.8 week wait time to see a specialist in Manitoba can be more devastating than the injury itself.(1) This paper aims to investigate the impact of implementing a physician assistant within existing sport and exercise medicine teams. METHODS: A questionnaire was sent off to six sport and exercise medicine physicians to understand the perceived scope of practice of a physician assistant, barriers to hiring a physician assistant, and preferred supervisory relationships. Using various sources, a comparison of each providers scope of practice was completed. A literature review was performed to evaluate physician assistant cost effectiveness and physician assistant’s effect on wait times. RESULTS: The sport and exercise medicine physicians showed limited understanding of the physician assistant’s full scope of practice. The most commonly expressed barrier to hiring a physician assistant was cost/funding. Only under direct supervision, physician assistants were trusted with the entire scope of practice of the supervising physician. In primary care, surgical and emergency cases, physician assistants have been shown to be cost effective if utilized to their full scope of practice. In the surgical setting, physician assistants showed the most significant reduction in wait times. CONCLUSION: Physician assistants have a broad scope of practice enabling them to extend the care of the attending physician and in some cases act as substitutes in the field of sport and exercise medicine. Since Manitoban sport and exercise medicine physicians do not know the entire scope of practice of physician assistants, this limits their ability to improve cost and wait times of the already existing sport and exercise medicine team.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".