Employees' Experiences of Workplace Violence: Raising Awareness of Workplace Stress, Well-being, Leadership, and Corporate Social Responsibility
Bibliographic record
Abstract
Abstract The sawmill shootings in British Columbia, Canada, resulted in fatalities and grievous injuries to workers, which have put a sensational face on workplace violence in the forestry sector. Yet, for all of the attention devoted after this horrific incident, to the growth and possible consequences of workplace violence, little empirical investigation has been done regarding the extent to which this type of violence may have permeated the sawmill forestry workplace in Canada; employees' experiences of workplace violence; employees' definition of workplace violence; the specific type of violence that occurs in sawmills; and the drivers of workplace violence as experienced and perceived by managers, union, and employees in the forestry sector context in British Columbia, Canada. This research critically explores these questions to better understand employees' experiences of workplace violence, the problems of violence and its implications for workplace stress, well-being, leadership, and corporate governance. This research contributes to the workplace violence body of knowledge as it relates to employment in the forestry sector in British Columbia, Canada. A mixed methodological approach was adopted using 367 questionnaire survey, 20 telephone interviews, and 2 focus groups lasting 45–60 minutes (managers and employees) were used to focus on managers, union, and employees' accounts of their own experiences and perceptions of workplace violence. The analysis of the data in this study lends support to the conclusion that workplace violence waged against workers in the forestry sector is significantly different than the violence being perpetrated in other sectors and work settings. The findings further suggest that forestry workers work environment, communities, and activity contributes meaningfully to the differences in workplace violence experienced by Sawmill employees. Insights obtained from this research can be used to develop educational tools and resources, and new policies to foster workplace practices conducive to reducing drivers to workplace violence, towards a more respectful workplace and overall employee well-being.
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 | 0.002 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".