Nursing emotion work and interprofessional collaboration in general internal medicine wards: a qualitative study
Bibliographic record
Abstract
AIM: This paper is a report of a study to examine nursing emotion work and interprofessional collaboration in order to understand and improve collaborative nursing practice. BACKGROUND: Nursing standards identify collaborative practice as necessary for quality patient care yet many nurses are often reluctant to participate in interprofessional teams. Strategies intended to improve participation often fail which suggests that the factors underpinning nurses' disinclination towards interprofessional collaboration have yet to be understood. The concept of emotion work has not been applied to nursing interprofessionalism, and holds the potential to improve collaborative practice. Nursing emotion work is defined as the management of the emotions of self and others in order to improve patient care. METHODS: Qualitative data were collected in 2006 using non-participant observation, shadowing and semi-structured interviews with nursing, medical and allied professionals in the general internal medicine wards of three hospitals in urban Canada. FINDINGS: Nurses' collaborations with other professionals are influenced by emotion work considerations. The establishment and maintenance of a nursing esprit de corps, corridor conflicts with physicians, and the failure of the interdisciplinary team to acknowledge the importance of nursing's core caring values are important factors underpinning nurses' interprofessional disengagement. CONCLUSION: Longstanding emotion work issues must be addressed before nurses will engage collaboratively. We suggest improving nursing collaboration through the refining of holistic nursing information, and reflections on practice by all interprofessional team members.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".