Promoting holistic approaches to management of violence in the ED—a response to Ramacciati (2023)
Bibliographic record
Abstract
We were very pleased to receive a response to our paper (Timmins et al., 2023) that highlighted current issues in relation to the nursing management of emergency department (ED) violence (Ramacciati, 2023). Moreover that the position paper achieved its primary aim in terms of “excellently achieved its goal of stimulating reflection on the causal factors of violence against nurses in emergency departments and discussing possible solutions and strategies for largely unresolved issues” (Ramacciati, 2023). This was most heartening for us. Understandably, this author echoes the growing concern among the nursing profession that there is a potential increase in this phenomenon, with growing frustration about ways to tackle the problems. They also, like us, agree that the management approach needs to be holistic taking account of the multiplicity of personal and contextual factors that contribute to its occurrence. Indeed, we drew upon the important work of Ramacciati et al. (2018) within our paper, noting that from an extensive study of Italian nurses (n = 15,000), these authors were able to determine a conceptual framework for understanding ED violence which gave equal weighting to the importance of environmental and organisational factors alongside factors within the nurse and patient considerations. The potential for the nurse to trigger violence, either by presence or action, was an interesting consideration to emerge from this conceptual work, and one that must be given careful consideration in management and policy approaches for the future. Certainly, as we pointed out in our paper, there are key contributing patient factors such as intoxication and cognitive/mental health issues, however the contribution of the environment, including other key issues such as overcrowding and queuing needs greater consideration in approaches to the management of ED violence. In fact, ED nurses, when they are asked about triggers for ED violence state overwhelmingly that the environment is the major contributory factor (Angland et al., 2014). Ramacciati (2023) points out that the prevention and management of ED violence has been high on policy makers agendas for more than two decades, and yet the health services appear stifled in their success with operationalising these and reducing and addressing the issues. There are many reasons for this, including under-reporting, acceptance of ED violence as part of the job and nurses only seeking redress when real harm has been done (Timmins et al., 2023). However, Ramacciati (2023) reminds us that it is time to evoke considered action and begin to examine and address this phenomenon in a way that comprehensively considers the multifactorial contributors within the ED dynamic and one that fully supports both nurses and clients in their care. Indeed, the work of these authors (Ramacciati, 2023) is sure to make a significant contribution to this, first by their important conceptual outline that can inform our understanding of the phenomenon, but also through other more recent work including the development of a user-friendly system (a smartphone app) for reporting ED violence (Ramacciati et al., 2021).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.030 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".