Prevalence and Determinants of Workplace Violence Against Nurses in the Italian Home Care Settings: A Cross‐Sectional Multicentre Study
Bibliographic record
Abstract
AIMS: To describe the prevalence and determinants of workplace violence against nurses in the Italian home care setting. DESIGN: Secondary cross-sectional analysis of data from the multicentre study AIDOMUS-IT. METHODS: Nurses employed in home care services provided by Italian Local Health Authorities were interviewed using a variety of instruments. A multivariable binary logistic regression model was performed to model the risk of workplace violence against nurses in the last 12 months. Variables related to violence were selected among sociodemographic characteristics (such as age and gender), work-related factors (including years of experience, team composition, overtime working, previous experience in mental health care, burnout) and organisational elements (including leadership and support, workload, staffing and resources adequacy, and time to reach the patients' homes). Adjusted odds ratios (aOR) were used to present the results. RESULTS: A total of 3949 nurses participated in the study and 20.49% of them reported to have experienced an episode of violence in the last 12 months. Determinants of higher risk of violence episodes were younger age (aOR = 1.02, p = 0.002), higher workload (aOR = 1.01, p = 0.002), working in a multiprofessional team (aOR = 1.24, p = 0.018), perception of inadequate managerial leadership and support (aOR = 1.38, p = 0.003), and higher burnout levels (aOR = 1.01, p < 0.001). CONCLUSION: The prevalence of workplace violence against Italian home care nurses is high. Several modifiable determinants were found to be associated with a higher risk of violence, which can potentially be mitigated with tailored interventions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Effective preventive strategies must be developed to lessen workplace violence against nurses in the home care setting. These strategies should focus on strengthening nursing managers' leadership and support skills, enhancing team-building strategies, avoiding inadequate workload, monitoring nurses' burnout, estimating optimum staffing levels, and assigning advanced-career nurses to home care services. These measures are imperative to guarantee the quality and safety of home care organisations and to attain favourable outcomes in the provision of care. IMPACT: This study aimed to explore the prevalence and determinants of workplace violence against nurses in the Italian home care settings. We found that out of the 3949 nurses surveyed, 20% of the sample reported one episode of violence during the last 12 months. Determinants of this violence included younger age, higher workload and burnout, being in a multiprofessional team, and perception of lack of leadership and support by the nurse manager. The results of this study can be used to tailor interventions aimed at mitigating the risk factors of violence, particularly those that can be modified (e.g., workload, burnout, and leadership). REPORTING METHOD: The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".