Interpersonal interactions, workplace violence, and occupational health outcomes among social workers
Bibliographic record
Abstract
Summary Primary emphasis within the literature on mechanisms to address the prevalence of negative occupational well-being outcomes among human service workers has tended to focus on individual self-care efforts or organizational level policies aimed at improving work–life balance. While these are important areas of research, the workplace setting itself can also create negative outcomes, suggesting the need to adapt characteristics of this setting. One aspect of this workplace setting includes the dynamics of interpersonal interaction within the workplace. This study reports a multivariate analysis of the relationship between negative workplace interpersonal interactions (generally defined to provide a more holistic assessment of ways in which violence is manifested in the workplace) between workers and service users and between workers themselves and human service worker occupational outcomes. Findings In 2012, data were collected from a sample (n = 674) of human service workers in Alberta, Canada. This study finds a high prevalence of negative workplace interactions between workers, and that these experiences have consequences for worker experience’s with burnout and life satisfaction, and contributes to intentions to leave the workplace. Compounding negative interpersonal interactions between workers are particularly significant across all measured occupational outcomes. Applications These findings suggest the need for a workplace ‘settings-based’ approach to improve occupational well-being among workers. Utilizing a workplace ‘settings-based’ approach would place more emphasis on the processes and structure of day-to-day work within organizations to help alleviate negative occupational outcomes among workers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".