Interprofessional interventions that impact collaboration and quality of care across inpatient trauma care continuum: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite the recognized importance of interprofessional collaboration (IPC) in trauma care, healthcare professionals often work in silos. Interprofessional (IP) interventions are crucial for optimizing IPC and delivering high-quality care across clinical contexts, yet their effectiveness throughout the inpatient trauma care continuum is not well understood. Thus, this review aimed to examine the literature on the effectiveness of IP interventions on collaboration processes and related outcomes in inpatient trauma care. METHODS: We conducted a scoping review following Joanna Briggs Institute's methodology. We searched six databases for studies from the last decade on IP interventions in inpatient trauma care. Two independent reviewers categorized IP interventions (education, practice, organization) and extracted their impact on IPC processes and related outcomes (team performance, patient, organization). RESULTS: Of the 17,397 studies screened, 148 met the inclusion criteria. Most were cohort designs (72%), conducted in level I trauma centers (57%) and emergency departments (51%), and involved surgeons (56%) and nurses (53%). Studies focused on IP organization interventions (51%), such as clinical pathways; IP practice interventions (35%), such as trauma team activation protocols; and IP education interventions (14%) including multi-method education. IP practice interventions most effectively improved team performance results, while IP education interventions primarily improved IPC processes. Positive patient outcomes were limited, with few studies examining organizational effects. CONCLUSIONS: Significant advancements are still required in IP interventions and trauma care research. Future studies should rigorously explore the effectiveness of interventions throughout the inpatient trauma care continuum and focus on developing robust measures for patient and organizational outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".