Violence Prevention: Technology-Enabled Therapeutic Intervention
Bibliographic record
Abstract
BACKGROUND: Nurses are disproportionately prone to experience incidents of violent victimization. Despite the vast literature on violence in healthcare settings, few studies have identified effective violence prevention interventions. AIM: The aim of the study was to explore the experiences of nurses regarding the implementation of technology-based violence prevention interventions. METHODS: Qualitative data were collected through semi-structured focus groups and interviews with 11 nurses at Humber River Hospital. Interviews were audiotaped, transcribed and subjected to a content analysis to identify core themes from the data. RESULTS: Three themes were identified: reassurance of safety, an increase in proactive measures and limitations of technology. Nurses held positive perceptions of the impact of technology-based interventions on violent incidents. The interventions were regarded as effective for the detection of potentially violent patients as well as for providing assistance from security staff when a violent incident occurs or appears imminent. However, nurses also acknowledged that patient-related violence was "unavoidable" and that technology cannot fully prevent violence from occurring. CONCLUSION: The findings from this study support the replication of these interventions in other healthcare facilities. Engaging staff, patients and families in this unique digital and technology-enriched environment has been critical for the successful implementation of the violence prevention electronic flagging system. Patient and family education and communication were essential for addressing concerns related to "labelling."
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".