Violence against emergency healthcare workers: different perpetrators, different approaches
Bibliographic record
Abstract
Purpose This study aims to investigate whether emergency health-care workers distinguish between different categories of perpetrators of violence and how they respond to different types of perpetrator profiles. Design/methodology/approach Five focus groups with emergency health-care workers were held in Canada. The participants were asked whether they identified different groups of perpetrators of violence and how that impacted their approach. The focus group responses were transcribed verbatim and analysed thematically using a phenomenological approach. Findings Participants consistently identified five groups of perpetrators and tailored their approach on their assessment of the type of perpetrator involved. The five categories are: violence or aggressive behaviour from family members or bystander and violence related to; underlying mental health/illness issues; underlying physical health issues; addiction and substance use; and repeat visitors/offenders. Violence with an underlying (mental) health cause was handled professionally and compassionately by the health-care workers, while less patience and understanding was afforded in those instances where violence was associated with (recreational) alcohol or illicit substance use. Originality/value Emergency health-care workers can consistently distinguish between types of perpetrators of violence and aggression, which they then use as one factor in the clinical and situational assessments that inform their overall approach to the management incidents. This conclusion supports the need to move the focus away from the worker to the perpetrator and to an organisational rather than individual approach to help minimise violence against emergency health-care 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".