Social Service Worker Experiences with Direct and Indirect Violence When Engaged with Service Users
Bibliographic record
Abstract
Abstract Social service workers’ experiences of violence from service users (client-engaged violence) in social service workplaces are serious and pervasive issues that demand responsive and effective organisational interventions. However, organisational factors and characteristics that have an effect on worker experiences of client-engaged violence are poorly defined. This study utilised a quantitative design to identify and measure aspects of the organisation that prevent client-engaged violence and support workers in building healthy and safe workplaces. Participants (n = 1,574) from various publicly administered social services departments were surveyed to assess the effect of ‘workload’ (workload quality); ‘supervisory dynamics’ (equality, involvement, support and attentiveness); ‘team dynamics’ (intrapersonal team functioning and interpersonal team functioning) and ‘workplace safety culture’ (workplace safety values) on direct and indirect experiences of client-engaged violence. Results from multivariate analysis show that workload characteristics and organisational cultural values of workplace safety had a significant effect on worker experiences of client-engaged violence. The results highlight the importance of creating organisational policies and procedures that support workers in managing workloads and promoting a culture of safety within the work setting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| 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".