Does the Type of Exposure to Workplace Violence Matter to Nurses’ Mental Health?
Bibliographic record
Abstract
Workplace violence is a prevalent phenomenon in healthcare, particularly among nursing professionals. Exposure to workplace violence may be direct through firsthand involvement, indirect through secondhand witnessing, or both. Even though implications for victims of workplace violence have been well-studied, less is known about the various types of exposure and their effects on nurse mental health. The purpose of this study is to examine the impact of workplace-violence exposure types on the mental health of nurses, while accounting for the intensity of the incident/s. This study employs an exploratory correlational design with survey methods. Nurses from British Columbia (BC), Canada, were invited by the provincial nurses' union to complete an electronic survey in Fall 2019. A total of 2958 responses from direct-care nurses in acute-care settings were analyzed using logistic regression. The results showed that mental-health problems increased with cumulative exposure; even though nurses with solely indirect exposure to workplace violence did not report greater mental-health problems, those experiencing solely direct exposure, or both direct and indirect exposure, were two to four times more likely to report high levels of post-traumatic stress disorder (PTSD), anxiety, depression and burnout compared to their counterparts with no exposure. There is an urgent need for better mental-health support, prevention policies and practices that take into account the type of workplace-violence exposure.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".