The Evolving Role of Physician Assistants in Multidisciplinary Healthcare Teams: A Focus on Interprofessional Collaboration in ICUs
Bibliographic record
Abstract
Critical care environments are increasingly challenged by rising patient acuity, workforce shortages, and the growing need for coordinated, team-based care. This review synthesizes evidence from various complementary studies examining: (1) interprofessional collaboration (IPC) challenges in South African intensive care units (ICUs), (2) the integration and impact of physician assistants (PAs) across diverse Canadian clinical settings, and (3) national workforce trends of PAs in United States critical care medicine. The findings reveal persistent organizational and system-level constraints—including staffing deficits, communication gaps, hierarchical barriers, and role ambiguity—that hinder effective collaboration. Evidence from Canada demonstrates that well-integrated PAs enhance workflow efficiency, continuity of care, and patient access, while U.S. data highlight a rapidly expanding PA critical care workforce with increasing postgraduate training, high job satisfaction, and substantial contributions to ICU operations. Collectively, the studies underscore the importance of structured IPC, comprehensive specialty preparation, and supportive organizational environments in strengthening ICU performance. Addressing burnout, improving role clarity, and expanding training opportunities remain essential for building a resilient and sustainable critical care workforce capable of meeting growing global demands.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".