Workplace violence against registered nurses: an interpretive description
Bibliographic record
Abstract
Health personnel, especially nurses, are often victims of workplace violence. Unfortunately, little is known about the nurses' experience of violence. A research study was initiated to further explore the nurses' accounts of workplace violence so as to make dimensions of the nurses' experience visible and more fully understood. Interpretive description was the research methodology adopted for this study. Using theoretical sampling, ten Registered Nurses from the lower mainland and Vancouver Island, British Columbia participated in semi structured, audiotaped interviews. In this research, the nurses' experience of workplace violence emerged as a highly complex entity, deeply embedded in relationships and context. How nurses perceive the contextual factors of the organization, their immediate work environment and their individual attributes were found to play a significant role in how they respond to the phenomenon. The findings of this study suggest that organizational culture is an important determinant in managing workplace violence and that policy and administrative personnel play a pivotal role in influencing the problem. Nursing culture also influences the nurses' expectations, assumptions and actions towards violence. Participants voiced that role conflict often challenged their ability to enact acquired professional ideals and that that they routinely undertake roles in dealing with violence that are not appropriate to their level of knowledge or skill. Within the nurses' immediate work environment, bullying as well as physical and verbal abuse was commonplace. Overcrowding, long waits for service, poor environmental design and inadequate staff to patient ratios were seen as factors that increased nurses' risk. Individual factors were associated with emotional and psychological harms that nurses endured. Workplace violence affected self-concept, self-esteem, self-efficacy and the nurses' sense of control. Moral distress, self-blame, feelings of failure, loss of motivation and leaving the nursing profession were significant findings. The results of this study demonstrate a need to re-think how we can address workplace violence in nursing. Research and intervention is needed to further explore organizational policy and governing structures, the culture and climate of practice environments, and the fundamental role nursing education programs have in preparing nurses to manage workplace violence.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".