Predictors of trivialization of workplace violence among healthcare workers and law enforcers.
Bibliographic record
Abstract
This study aims to identify individual and organizational predictors of trivialization of violence in 2 work sectors: healthcare and law enforcement. On the basis of data from a survey conducted among 1,141 workers from healthcare (e.g., nurses, orderlies.) and law enforcement (e.g., police, security agents), individual (sex, age, exposure to violence), and organizational factors (violence prevention training, support from colleagues and supervisors, presence of a “zero tolerance” policy and safety of physical environment) were used to predict trivialization of violence. Analyses were also conducted separately for women and men, and post hoc comparisons of regression estimates were performed to assess sex differences. Men were more likely than women to think that violence is normal in their workplace. Law enforcers were more likely than healthcare workers to perceive a taboo associated with complaining about workplace violence. This last result was most salient in the model with women, where the odds of perceiving a taboo associated with complaining about workplace violence were 2 times higher among law enforcers. Organizational factors were all significant negative predictors of perceiving a taboo associated with complaining about workplace violence. Trivialization was also positively associated with witnessing violent acts, not with being direct victim of workplace violence. By identifying factors that hinder work-related threat assessment and management, this study showed that organizations can decrease or prevent trivialization of workplace violence. Organizations may counter underreporting of this threat, which will increase capacity to assess the real magnitude of this problem and to better manage it
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".