“Go back to your country”: Exploring nurses' experiences of workplace conflict involving patients and patients' family members in two Canadian cities
Bibliographic record
Abstract
This study explores nurses' experiences of workplace conflict with patients and their family members, how it differs by ethnic/racial identity, and highlights the coping strategies engaged to lessen these conflicts. Using a qualitative research design, this study draws on phenomenology and in-depth interviews of 66 registered nurses and registered practical nurses from multiple sites in two Canadian cities to explore the experiences of nurses with multiple marginalized identities in relation to nurse-patient and nurse-patient's family member conflicts in direct care practice. The results show that horizontal conflicts, especially, ones involving nurses, patients, and their family members are quite pervasive in the nursing profession. Direct care nurses, especially, ethnic minorities relative to majority groups experience excessive physical assaults, verbal aggressive behaviors, racial stereotyping and discrimination, and sexual harassment from patients and patients' family members. Institutional support through policies and practices designed to de-escalate aggressive behavior from patients and their family members were identified as important support systems. We conclude that policies aimed at creating a safe and strong health-care system call for holding patients and th'eir family members legally responsible for uncivil and aggressive behavior against caregivers.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".