Potential of physician assistants to support primary care Evaluating their introduction at 6 primary care and family medicine sites
Bibliographic record
Abstract
OBJECTIVE: To determine effective strategies for introducing physician assistants (PAs) in primary care settings and provide guidance to support ongoing provincial planning for PA roles in primary care. DESIGN: Time-series research design using multiple qualitative methods. SETTING: Manitoba. PARTICIPANTS: Physician assistants, supervising family physicians, clinic staff, members of the Introducing Physician Assistants into Primary Care Steering Committee, and patients receiving care from PAs. METHODS: The PA role was evaluated at 6 health care sites between 2012 and 2014; sites varied in size, funding models, geographic locations (urban or rural), specifics of the PA role, and setting type (clinic or hospital). Semistructured interviews and focus groups were conducted; patient feedback on quality improvement was retrieved; observational methods were employed; and documents were reviewed. A baseline assessment was conducted before PA placement. In 2013, there was a series of interviews and focus groups about the introduction of PAs at the 3 initial sites; in 2014 interviews and focus groups included all 6 sites. MAIN FINDINGS: The concerns that were expressed during baseline interviews about the introduction of PAs (eg, community and patient acceptance) informed planning. Most concerns that were identified did not materialize. Supervising family physicians, site staff, and patients were enthusiastic about the introduction of PAs. There were a few challenges experienced at the site level (eg, front-desk scheduling), but they were perceived as manageable. Unanticipated challenges at the provincial level were identified (eg, diagnostic test ordering). Increased attachment and improved access—the goals of introducing PAs to primary care—were only some of the positive effects that were reported. CONCLUSION: This first systematic multisite evaluation of PAs in primary care in Canada demonstrated that with appropriate collaborative planning, PAs can effectively integrate into primary care settings in a short period of time, with high acceptance from stakeholders. Further research is required to measure the effects of introducing PAs to health care centres and to provide direction for future outcome assessments.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".