Barriers and Facilitators to Nurse-Provider Communication in the Emergency Department: A Scoping Review
Bibliographic record
Abstract
BackgroundEffective nurse-provider communication in the emergency department (ED) is crucial but often hindered by hierarchical dynamics and high workloadsObjectivesThis review aimed to examine, systematically map, and identify gaps in existing literature concerning ED registered nurse-provider communication.Eligibility CriteriaStudies focused on direct communication between bedside registered nurses (RNs) and providers, including physicians, physician assistants (PAs), and nurse practitioners (NPs) in the ED, encompassing verbal, non-verbal and electronic communication. Non-direct patient care roles were excluded. Sources of Evidence: A search in CINAHL, MEDLINE and ProQuest Nursing & Allied Health identified 1978 sources, of which 37 studies were included: 15 qualitative, 9 quantitative, 4 mixed methods, 6 commentaries, 1 performance improvement project, and 1 scoping review.Charting MethodsData were extracted using Joanna Briggs Institute (JBI) guidelines and thematically analyzed according to Levac's framework.ResultsFour key themes emerged: (i) Interruptions hinder communication but can be reduced by electronic supports facilitating asynchronous communication, (ii) Power imbalances and high workload/communication load impede effective communication, emphasizing the need for structured communication tools and interprofessional communication training, (iii) Shared workspaces, electronic supports, and collaborative, respectful interactions enhance communication (iv) Timely updates and collaborative planning are valued, emphasizing the significance of consistent communication.ConclusionsThis review identified interventions that can improve ED nurse-provider communication, including electronic supports, shared workspaces, structured communication tools, and interprofessional communication training. Future research should evaluate these strategies' effectiveness and explore regional differences, particularly in Canada, where the literature is limited.
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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 | 0.026 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".