The Effects of Trivialization of Workplace Violence on Its Victims: Profession and Sex Differences in a Cross-Sectional Study among Healthcare and Law Enforcement Workers
Bibliographic record
Abstract
BACKGROUND: Workers from the law enforcement and healthcare sectors tend to normalize or mute their victimization from workplace violence (WPV). OBJECTIVES: This study aims to assess the impact of the trivialization of WPV on psychological consequences for workers who have been affected by a WPV incident. The second aim is to assess the moderating effect of sex on the trivialization of WPV. The third and overarching aim is to assess the moderating effect of professional identity on the relations between individual and organizational factors and psychological consequences following a WPV incident. METHODS: The findings are based on a convenience sample of 377 (204 female and 173 male) workers from the law enforcement and healthcare sectors. Individual factors (sex, age, professional identity, prior victimization, witnessing WPV, injuries, and trivialization of violence) and perceived support factors (colleagues' support and employer's support) were used as predictor variables of psychological consequences in hierarchical linear regression models. Sex was used as a moderator of trivialization while professional identity was used as a moderator of all predictors. FINDINGS: When individual and social support factors were controlled for, normalizing violence was negatively associated with psychological consequences while perceiving a taboo associated with complaining about WPV was positively associated for all participants. When these relations were moderated by the sex of the participants and then by their professional identity, normalization was found to decrease psychological consequences only for male healthcare workers. IMPLICATIONS: To help employees cope with WPV, organizations should promote strategies adapted to profession and sex differences. For male healthcare workers, normalization as a cognitive coping strategy should be formally recognized. For both professions and sexes, organizational strategies that counter the perceived taboo of complaining about violence should be reinforced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".