Enhancing Evidence-Based Practice Implementation in Acute Care: A Qualitative Case Study of Nurses’ Roles, Interprofessional Collaboration, and Professional Development
Bibliographic record
Abstract
Background and PurposeEvidence-based practice (EBP) is essential for improving patient outcomes and healthcare quality. However, its implementation in acute care remains inconsistent due to organisational hierarchies, professional silos, and limited access to continuous professional development (CPD). Nurses play a key role in translating research into practice but often encounter barriers that limit their ability to lead EBP initiatives. Interprofessional collaboration and CPD are recognised enablers of EBP, yet their impact in acute care requires further investigation. This study explores how interprofessional collaboration, nurse-led initiatives, and CPD influence EBP adoption.Methods and ProceduresA collective qualitative case study was conducted across two acute care hospitals in the East Midlands, England. Data collection included 25 semi-structured interviews, nonparticipant observations, and document analysis over six years, with an intensive fieldwork phase in 2022. Thematic analysis was used to identify key patterns related to EBP adoption, interprofessional collaboration, and nurse-led knowledge implementation.ResultsNurses actively advocated for EBP integration but often worked independently due to the absence of formal collaboration structures. Interprofessional collaboration facilitated knowledge-sharing and decision-making, yet hierarchical constraints limited nurses' influence in clinical governance. CPD enhanced nurses' confidence and ability to challenge outdated practices, but disparities in access led to inconsistent EBP engagement across nursing teams.ConclusionStructured CPD, interdisciplinary collaboration, and inclusive decision-making are essential for EBP adoption. Addressing hierarchical constraints and resource limitations is crucial for sustaining evidence-driven care. Future research should explore the long-term sustainability of EBP implementation.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".