The Role of Interpersonal Relations in Healthcare Team Communication and Patient Safety
Bibliographic record
Abstract
Patient safety is compromised by medical errors and adverse events related to miscommunications among healthcare providers. Communication among healthcare providers is affected by human factors, such as interpersonal relations. Yet, discussions of interpersonal relations and communication are lacking in healthcare team literature. This paper proposes a theoretical framework that explains how interpersonal relations among healthcare team members affect communication and team performance, such as patient safety. We synthesized studies from health and social science disciplines to construct a theoretical framework that explicates the links among these constructs. From our synthesis, we identified two relevant theories: framework on interpersonal processes based on social relation model and the theory of relational coordination. The former involves three steps: perception, evaluation, and feedback; and the latter captures relational communicative behavior. We propose that manifestations of provider relations are embedded in the third step of the framework on interpersonal processes: feedback. Thus, varying team-member relationships lead to varying collaborative behavior, which affects patient-safety outcomes via a change in team communication. The proposed framework offers new perspectives for understanding how workplace relations affect healthcare team performance. The framework can be used by nurses, administrators, and educators to improve patient safety, team communication, or to resolve conflicts.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".