Nurses Perceptions of the Utilization of the Violence Assessment Tool (VAT) in Northeastern Ontario
Bibliographic record
Abstract
Workplace violence (WPV) is an ongoing problem in health care. Most of the cases of WPV are caused by the patients, patients’ families, and friends. Violence in hospitals among registered nurses has led to 56% of lost time injuries, and in 2018, Ontario’s Workplace Safety and Insurance Board (WSIB) reported 13% of lost time injuries due to WPV. The Public Services Health and Safety Association (PSHSA) created the Violence Assessment Tool [VAT] to predict the possible risk of violence from patients in acute care settings. Health care workers can use the VAT to assess risk, apply possible control measures and improve their safety. As part of a larger study, the aim of this research is to explore nurses’ perceptions of the utilization of the VAT in assessing the potential risk of violence, and to identify any gaps, challenges, or improvements needed in the VAT. An Interpretive Description research design by Sally Thorne in (2016) will be used. The model that will guide this study is the Haddon Matrix framework of workplace violence prevention. The study will involve three focus groups via zoom virtual meetings with 6 to 8 participants per session, and an expected total of 18-24 participants. Focus group interviews will use semi-structured questions to guide the discussion among nurses working in a Northeastern Ontario hospital. Interpretive description data analysis will be guided by Thorne’s processes of data analysis. This will be the first study to examine nurses’ perceptions of the VAT in Ontario. The findings of this study will help to determine the predictive validity of the VAT and any potential changes that may be needed. The findings of this study could lead to reduced violence and associated costs within the healthcare sector.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".