Deconstructing contributing factors to bullying and lateral violence in nursing using a postcolonial feminist lens
Bibliographic record
Abstract
Bullying and lateral violence is a reality in the workplace for many nurses and has been explored in nursing literature for at least three decades. Using a postcolonial feminist approach this paper examines what contributes to bullying and lateral violence in the nursing workplace by deconstructing the findings from a British Columbia Nurses Union and Union of Psychiatric Nurses study. Theories of oppression and organizational context which have appeared in the literature serve to inform the discussion. A postcolonial lens provides an opportunity to come to grips with the insidiousness of bullying and lateral violence. An adaption of Phillips, Lawrence, and Hardy's (2004) framework is used to unpack discourses, actions, texts, and organizational practices to challenge taken-for-granted hegemonies in the workplace. Taking this different view has enabled new prisms of understanding to emerge from the contributing factors identified in the study. Based on this analysis it is clear that bullying and lateral violence is deeply institutionalized. Nurses, managers, and organizations need to interrupt and interrogate the embeddedness of bullying and lateral violence in order to create a civil workplace.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".