Bibliographic record
Abstract
Nursing managers are identified as playing a central role in workplace aggression management. In effect, employees' decisions to report unacceptable behavior is said to be directly influenced by how a manager will respond to their claims. Using principles from critical nursing ethnography, data were collected from interviews, organizational documents, and observation of physical environment. Twenty-three semistructured interviews were conducted in both a university-affiliated psychiatric hospital and a community hospital located in a large metropolitan city in Ontario. The study aimed at broadening the understanding of how nurse managers respond to intraprofessional and interprofessional workplace aggression. Several strategies were described by managers including coaching individuals so they feel capable of addressing the issue themselves, acting as mediator to allow both sides to openly and respectfully talk about the issue, and disciplining employees whose actions warrant harsh consequences. As part of the study, managers reported that dealing with workplace aggression could be difficult and time consuming and admitted that they sometimes came to doubt their abilities to be able to positively resolve such a widespread problem. Conclusions drawn from the study suggest that aggression management is not solely the responsibility of managers but must involve several actors including the aggressive individual, peers, human resources department, and unions.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".