Improving Health Care Access in Primary Care Through Physician Assistant (PA) Integration: A Literature Review
Bibliographic record
Abstract
Introduction: Canada is currently experiencing a shortage of primary care physicians. Lack of access to primary care results in increased morbidity, mortality, and increased burden on the healthcare system. A proposed solution has been introducing team-based care, which includes Physician Assistants (PAs). The implementation of PAs in the primary care setting and the impact on patients’ access to health services and patient care is still vastly under researched. Objective: This literature review will examine the impact of PAs in primary care settings to improve patient access specifically looking at wait times. Further, this review will examine patients’ perspectives on being treated by a PA in the primary care setting. These objectives will directly address if PAs could be a proposed solution to the primary care crisis. Methods: A literature search was completed using PubMed, and Medline databases. The search was performed using key terms about physician assistants working in primary care settings and patient perspectives. Six articles were found to meet the inclusion criteria and were analyzed for this review. Results: Six studies explored the role of PAs in primary care settings across North America and England. Four themes were found including improved patient access, patient satisfaction, patient awareness, and patient experiences. These themes were used to examine patient perspectives and the roles of PAs in supporting the primary care setting. The findings suggest that PAs improve patient access in primary care and that patients’ experiences are overall positive with PAs in the primary care setting. Conclusions: These findings suggest that PAs are a potential solution to the primary care crisis. The Physician Assistant programs are expanding across Canada, and the number of practicing PAs in Canada is set to exponentially grow. For PAs to be implemented in primary care, primary research on PAs working in these settings should be prioritized to examine the potential positive benefits on the healthcare system and primary care access.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".