Physician Assistant Involvement in Health Advocacy, Health Promotion and Disease Prevention: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Physician Assistants (PAs) have been integrated into the Canadian healthcare system to improve patient access and clinical efficiency. The CanMEDS-PA framework describes the PA as a health advocate, but the current extent of PA involvement in health advocacy has not been delineated. A scoping review was conducted to investigate PA participation in health advocacy, health promotion and disease prevention initiatives. METHODS: An electronic literature search was conducted using Web of Science, PubMed, CINAHL, OVID (Embase and MEDLINE) and Cochrane databases. Broad eligibility criteria were used to include publications involving PAs or PA students who participated in health advocacy, health promotion and disease prevention initiatives globally. RESULTS: 297 records were identified; 14 met the inclusion criteria.  Publications included cross-sectional studies, surveys, program evaluations, clinical framework development, and patient education handouts. Topics included cancer screening, chronic disease management, adolescent health promotion and stroke prevention.  All records were published in the United States. There was an overall positive contribution of PAs to health advocacy, health promotion and disease prevention. Several specific limitations were noted related to procedural techniques and continuity of practice. CONCLUSION: Global research on PA involvement in health advocacy, health promotion and disease prevention is limited and focuses on a small subset of medicine (cancer screening) in one geographical area (United States). Data show that PAs are effective health advocates but more reporting is needed to guide expansion of the PA role and to inform policy in Canada and globally.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".