Workplace violence against emergency health care workers: What Strategies do Workers use?
Bibliographic record
Abstract
Background: Workplace violence by patients and bystanders against health care workers, is a major problem, for workers, organizations, patients, and society. It is estimated to affect up to 95% of health care workers. Emergency health care workers experience very high levels of workplace violence, with one study finding that paramedics had nearly triple the odds of experiencing physical and verbal violence. Many interventions have been developed, ranging from zero-tolerance approaches to engaging with the violent perpetrator. Unfortunately, as a recent Cochrane review showed, there is no evidence that any of these interventions work in reducing or minimizing violence. To design better interventions to prevent and minimize workplace violence, more information is needed on those strategies emergency health care workers currently use to prevent or minimize violence. The objective of the study was to identify and discuss strategies used by prehospital emergency health care workers, in response to violence and aggression from patients and bystanders. Mapping the strategies used and their perceived usefulness will inform the development of tailored interventions to reduce the risk of serious harm to health care workers. In this study the following research questions were addressed: (1) What strategies do prehospital emergency health care workers utilize against workplace violence from patients or bystanders? (2) What is their experience with these strategies? Methods: Five focus groups with paramedics and dispatchers were held at different urban and rural locations in Canada. The focus group responses were transcribed verbatim and analyzed using thematic analysis. Results: It became apparent that emergency healthcare workers use a variety of strategies when dealing with violent patients or bystanders. Most strategies, other than generic de-escalation techniques, reflect a reliance on the systems the workers work with and within. Conclusion: The study results support the move away from focusing on the individual worker, who is the victim, to a systems-based approach to help reduce and minimize violence against health care workers. For this to be effective, system-based strategies need to be implemented and supported in healthcare organizations and legitimized through professional bodies, unions, public policies, and regulations.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".