Perceived aggression towards nurses: study in two Italian health institutions
Bibliographic record
Abstract
AIMS: The goal of the present study was to quantify the perceived aggression towards nurses working in two Italian health care institutions and to verify the hypothesis of an association between the characteristics of aggressors and the type of aggression. BACKGROUND: Violence and aggressiveness, particularly aimed at nurses, are a common, but inadequately investigated phenomenon in Italian health care institutions. DESIGN: A cross-sectional study. METHODS: The study was performed, studying a sample of 700 nurses (37% of the personnel in 94 units) in two health care institutions in northeast Italy using an anonymous multiple-choice questionnaire. RESULTS: Forty-nine percent of the nurses responded that they had experienced aggression in the previous year, 82% of that was only verbal. This happened more often to female nurses working in the emergency department and in geriatric and psychiatric units. A statistically significant association (p < 0.001) was found between the perception of fatigue, stress and work dissatisfaction and the frequency of aggression. Aggressors were usually patients or their relatives (57%) and were mainly men (66%). Fifty-three percent of assaulted nurses did not ask for help after the event. CONCLUSIONS: This study confirms the high incidence of perceived, mainly verbal aggression towards nurses. RELEVANCE TO CLINICAL PRACTICE: Action to prevent aggressive episodes may include concentrating on job motivation, encouraging participatory leadership and promoting the best possible working conditions. The absence of any systematic event reporting and documentation makes the assaulted workers feel defenceless.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".