Workplace Violence Prevention: Flagging Practices and Challenges in Hospitals
Bibliographic record
Abstract
BACKGROUND: Flagging is a standardized way to communicate the risk of patient violence to workers. We add to the limited body of research on flagging by describing hospitals' approaches to and challenges with flagging patients with a history of violent behavior. METHODS: We used a qualitative case study approach of hospitals in Ontario, Canada and their patient flagging practices. Key informants and our advisory committee identified 11 hospitals to invite to participate. Hospitals assisted in recruiting frontline clinical and allied health workers and managers to an interview or focus group. A document analysis of hospitals' flagging policies and related documents was conducted. Thematic analysis was used to analyze interview and focus group data. FINDINGS: = 15). Participants described three challenges: patient stigmatization, patient privacy, and gaps in policy and procedures. CONCLUSION/APPLICATION TO PRACTICE: Flagging patients with a history of violent behavior is one intervention that hospitals use to keep workers safe. While violence prevention was important to study participants, a number of factors can affect implementation of a flagging policy. Study findings suggest that hospital leadership should mitigate patient stigmatization (real and perceived) and perception of patient rights infringement by educating all managers and frontline workers on the purpose of flagging and the relationship between occupational health and safety and privacy regulations. Leadership should also actively involve frontline workers who are the most knowledgeable about how policies work in practice.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".