Interprofessional communication between surgery trainees and nurses in the inpatient wards: Why time and space matter
Bibliographic record
Abstract
Optimal interprofessional communication (IPC) is broadly viewed as a prerequisite to providing quality patient care. In this study, we explored the enablers and barriers to IPC between surgical trainees and ward nurses with a view towards improving IPC and the quality of surgical patient care. We conducted an ethnography in two academic centres in Canada totalling 126 hours of observations and 32 semi-structured interviews with trainees and nurses. Our findings revealed constraints on IPC between trainees and nurses derived from contested meanings of space and time. Trainees experienced the contested spatial boundaries of the surgical ward when they perceived nurses to project a sense of territoriality. Nurses expressed difficulty getting trainees to respond and attend to pages from the ward, and to have a poor understanding of the nurses' role. Contestations over time spent in training and patient care were found in trainee-nurse interactions, wherein trainees perceived seasoned nurses to devalue their clinical knowledge on the ward. Nurses viewed the limited time that trainees spent in clinical rotation in the ward as adversely affecting communication. This study underscores that challenges to enhancing IPC at academic health centres are rooted in team and professional cultures. Efforts to improve IPC should therefore: identify and target the social and cultural dimensions of healthcare team member relations; recognise how power is deployed and experienced in ways that negatively impact IPC; and enhance an understanding and appreciation in the temporal and spatial dimensions of IPC.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".