Spot It, Prevent It: Evaluation of a Rapid Response Algorithm for Managing Workplace Violence Among Home Care Workers
Bibliographic record
Abstract
Background:Workplace violence incidents remain pervasive in health care. Home care workers like personal support workers (PSWs) provide services for clients with dementia, which has been identified as a risk factor for workplace violence. The objective of this study was to evaluate whether the implementation of a rapid response algorithm resolved unsafe working conditions associated with responsive behaviors and decreased perception of risk.Methods:A nonexperimental pre- and post-evaluation design was utilized to collect data from PSWs and supervisors. PSWs completed an online survey about their experience with workplace violence and perception of risk. Bi-weekly check-ins were conducted with supervisors to track incidents and their level of resolution in the algorithm. Semi-structured interviews were also conducted to gather in-depth feedback about the algorithm in practice.Findings:We found no difference in risk perception among PSWs pre- and post-implementation. However, PSWs who had been employed for less than 1 year had a significantly higher risk perception. Overall, the algorithm was found to be helpful in resolving workplace violence incidents.Conclusion and Application to Practice:Opportunity exists to further refine the algorithm and ongoing dissemination, and implementation of the algorithm is recommended to continually address incidents of workplace violence. Newly hired PSWs may require additional supports. Ongoing education and training were identified as key mitigation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 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 teacher head, 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".