Violence and aggression in mental health‐care settings
Bibliographic record
Abstract
Violence and aggression in mental health-care settingsWelcome to this special edition of International Journal of Mental Health Nursing, focussing on violence and aggression in mental health-care settings.While this is not a new agenda, to date, the journey travelled has been an interesting one, and the present agendas are quite different to those identified historically, albeit not in all aspects of care.For example, the use of the term 'restrictive practices' and the push for their minimization are relatively recent.As co-editors we first published our research in this area some 20 years ago when there was a distinct scarcity of research into coercive measures in mental health nursing practice (Muir-Cochrane 1995, 1996;Duxbury 1999Duxbury , 2002)).A lot has changed since then, and we offer some reflections to whet your appetite for this special issue.Aggression and violence are now a global concern in mental health settings.Early and timely recognition of the predictors of aggression and violence are recognized as crucial in facilitating the use of de-escalation strategies and avoidance of conflict situations (Jackson et al. 2014).Nevertheless, between 8% and 38% of health workers continue to suffer physical violence at some point in their careers (World Health Organization 2017), and we know that aggression and violence have a significant negative impact on the mental health and well-being of nurses, as well as their motivation to remain employed in nursing.As a response, health organizations in Australia have adopted a risk-based and zero tolerance approach to aggression and violence, which has effected how mental health nurses provide care.This approach continues to be used in Australia, although it is now considered dated and counterproductive in other countries.What we used to call 'needs assessment' in regard to planning care for consumers is now termed 'risk assessment'.This change occurred as safety discourses in health care (patient safety, quality assurance, and quality improvement) emerged (Selmon et al. 2017).Originally, these discourses referred to the protection of the patient from hospital 'harm', such as medication errors or poor communication at handover.However, the risk discourse has evolved differently in spaces where mental health consumers/service users are cared for
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".