Workplace violence against Bangladeshi registered nurses: A survey following a year of the COVID‐19 pandemic
Bibliographic record
Abstract
AIMS: To investigate the prevalence of workplace violence and its associated factors among Bangladeshi registered nurses. BACKGROUND: Workplace violence is prevalent among nurses, particularly in developing countries. However, the issue has never been examined in Bangladeshi nurses. METHODS: Between February 26 and July 10, 2021, this cross-sectional survey involving 1264 registered nurses was conducted. Workplace violence was determined by the Workplace Violence Scale (WVS). A multivariable logistic regression model was fitted to find the factors associated with workplace violence. This study complies with the EQUATOR (STROBE) checklist. RESULTS: Of the 1264 nurses, 885 (70%) nurses reported being exposed to workplace violence in the previous year. Three hundred twenty-four (324; 25.6%) nurses reported physical violence, whereas 902 (71.4%) nurses reported nonphysical violence. According to the multivariable logistic regression model, male nurses, nurses in the Sylhet division, emergency department nurses, nurses working extended hours, and non trained nurses to tackle workplace violence were prone to physical violence. Furthermore, public hospital nurses and non trained nurses to tackle workplace violence were more likely to be exposed to nonphysical violence. Nurses who had not been exposed to workplace violence were satisfied with their current job, but those who had been exposed to workplace violence were dissatisfied and intended to leave their current job. CONCLUSIONS AND IMPLICATIONS FOR NURSING AND HEALTH POLICY: High prevalence of workplace violence underscores nurses' current working conditions, which are particularly poor in public hospitals and emergency departments. Moreover, the COVID-19 pandemic put unprecedented pressure on the whole healthcare system and caused various difficulties for healthcare workers. To develop a zero-violence practice environment, health authorities should implement policy-level interventions. Healthcare staff should be guided to deal more successfully with patients and coworkers to create a positive working environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".