Iranian pre‐hospital emergency care nurses' strategies to manage workplace violence: A descriptive qualitative study
Bibliographic record
Abstract
AIM: To explore the experiences of Iranian nurses working in pre-hospital emergency care services and the strategies used to manage of workplace violence. BACKGROUND: Pre-hospital emergency nurses are subject to workplace violence; however, little research addresses their experiences, particularly related to their strategies in dealing with workplace violence. METHODS: A descriptive qualitative study that involved nineteen male nurses who were working in pre-hospital services collected data using semi-structured interviews and analysed it using qualitative content analysis. RESULTS: Data analysis yielded four descriptive categories including no reaction to violence (tolerance and acceptance as common workplace conflicts), situational management (patient and scene management), confrontation (direct and indirect) and escaping the scene. Patient management was the dominant strategy used and had the best outcomes related to both patient and personnel safety. CONCLUSION: This study showed that pre-hospital nurses use different strategies to manage violence and patient management was a common and useful strategy for managing workplace violence. However, the pre-hospital nurses have little training, insufficient support and are poorly prepared to manage workplace violence. IMPLICATIONS FOR NURSING MANAGEMENT: The development of context-based guidelines, continuing education, better-equipped ambulances that include medical and defence equipment, as well as better coordination of the police force in ambulance operations, can help to reduce workplace violence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".