Experiences of in-person violence and cyberviolence against child welfare workers in Canada
Bibliographic record
Abstract
Abstract Studies conducted in countries across the world have consistently demonstrated high rates of in-person violence towards social workers, and particularly social workers engaged in challenging areas of practice such as child welfare. More recently, research has focused on the cyberworld as a new avenue for abuse against workers. Employing an online survey, this research sought to compare the experiences and effects of in-person violence and cyberviolence among child welfare workers in Canada; and to better understand the influences of organizational factors on levels of distress experienced by workers. Respondents reported high levels of exposure to in-person violence, as well as high levels of abuse and threats through digital means. Among various forms of in-person violence, threats were associated with the highest level of traumatic stress symptoms. Other factors that were associated with traumatic stress were perceptions of organizational support and experiences of burnout, particularly emotional exhaustion, which was the best predictor of traumatic stress scores related to both in-person and cyberviolence. Given that cyberviolence is a new area of study, further research is necessary to more fully understand the ability of the workplace environment to mitigate harmful aspects of this recent form of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".