Using a Structural Lens to Understand and Address Aggression and Violence Experienced by Emergency Department Nurses: Beyond Individualistic Perspectives
Bibliographic record
Abstract
Aggression and violence toward nurses is a growing problem in Canadian emergency departments. Existing literature often examines this issue through an individualistic lens, focusing primarily on individual behaviors of patients and staff, with limited attention to organizational and structural factors contributing to root causes. This paper presents a secondary analysis of a larger data set - including interviews with hospital staff, observational field notes, and open-ended patient survey responses - to explore the structural and contextual factors shaping aggression and violence in emergency departments. Using a structural lens informed by critical theoretical perspectives and guided by interpretive description, the research team conducted a thematic analysis to identify recurrent patterns across the data sources. The analysis reveals how policies, power relations, and institutional norms shape the conditions that give rise to violence, moving beyond individual-level explanations. Three themes were identified: (a) significant stress and frustration is the contextual backdrop, (b) dominant norms and a culture of efficiency in the emergency department create and maintain a stressful environment, and (c) widespread health and social inequities and a lack of community resources exacerbate stress and frustration. This analysis demonstrates that addressing aggression and violence requires multi-pronged strategies that engage with the structural contexts shaping these events.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".