Northeastern Ontario nurses' perceptions of violence in acute care settings
Bibliographic record
Abstract
The incidence of workplace violence (WPV) is increasing and has become a worldwide \nconcern. This is particularly true among medical workers, especially nurses, who are at a high \nrisk of exposure, as they are the first and closest contact with patients. The Ontario Council of \nHospital Unions and the Ontario division of the Canadian Union of Public Employees conducted \na survey in Northeastern Ontario in 2019 and found that 96% of personal support workers and \nregistered practical nurses experienced physical violence while working. This was 8% higher \nthan the provincial average. This study explores Northeastern Ontario nurses’ perceptions of \nviolence in an acute-care setting through two research questions: What are Northeastern Ontario \nnurses’ perceptions of violence and challenges to preventing violence? What improvements or \nchanges are needed to reduce or prevent WPV? This study uses Sally Thorne’s (2016) \ninterpretive description qualitative methodology guided by the Haddon matrix conceptual \nframework of WPV. Registered nurses (n = 14) participated in one of three virtual focus groups \nfrom three patient care units. The overarching theme, nurses surviving violence in acute-care \nsettings, is supported by three key themes: nurses’ different perceptions and levels of threshold \nof violence, nurses in jeopardy, and changes needed to the status quo. The findings indicate that \nviolence against nurses occurs daily and should never be justified. Education, training, and \nsupports involving hospital staff, the local police department, the community, and the public are \ncrucial to preventing WPV.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".