Workplace violence and associated factors against nurses working in public hospitals in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The issue of workplace violence (WPV) directed at nurses is a chronic and global public health concern. Numerous studies on workplace violence in Ethiopia have been conducted; however, the results have been inconsistent. The review aims to identify the pooled prevalence and associated factors of workplace violence against nurses. METHODS: A systematic and methodical literature search was conducted using electronic databases such as Google Scholar, EMBASE I, Scopus, PubMed, HINAR, the Web of Science, and the African Journal Online (AJOL). Each original study's efficacy and quality were assessed using a modified Newcastle-Ottawa scale (NOS) technique designed for cross-sectional research. The Cochrane Q and I2 test statistics were used to verify the heterogeneity of the studies. Using a random effect model, the pooled estimate of workplace violence among nurses was calculated. RESULT: The pooled estimate of workplace violence among nurses in Ethiopia was 39.43% (95% CI: 27.63, 51.23). Female nurse (POR = 2.25; 95% CI: 1.29, 3.92), short work experience (POR = 3.25; 95% CI: 2.37, 4.45), and living without a spouse (POR = 2.03; 95% CI: 1.03, 3.99) were identified factors associated with workplace violence. CONCLUSION: This study found that about two-fifths of nurses encounter workplace violence. According to this study, there was a significant association between work place violence among Nurses and being female, having less job experience, and being single. To address this issue, the Federal Ministry of Health (FMOH), policymakers, and other stakeholders should prioritize interventions aimed at reducing workplace violence.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".