Workplace violence, bullying, burnout, job satisfaction and their correlation with depression among Bangladeshi nurses: A cross-sectional survey during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Depression is one of the most serious yet understudied issues among Bangladeshi nurses, bringing health dangers to this workforce. This study aimed to investigate how workplace violence (WPV), bullying, burnout, and job satisfaction are correlated with depression and identify the factors associated with depression among Bangladeshi nurses. METHODS: For this cross-sectional study, data were collected between February 26, 2021, and July 10, 2021 from the Bangladeshi registered nurses. The Workplace Violence Scale (WPVS), the Short Negative Acts Questionnaire [S-NAQ], the Burnout Measure-Short version (BMS), the Short Index of Job Satisfaction (SIJS-5), and the Patient Health Questionnaire (PHQ-9) were used to measure WPV, bullying, burnout, job satisfaction, and depression, respectively. Inferential statistics include Pearson's correlation test, t-test, one-way ANOVA test, multiple linear regression, and multiple hierarchal regression analyses were performed. RESULTS: The study investigated 1,264 nurses (70.02% female) with an average age of 28.41 years (SD = 5.54). Depression was positively correlated with WPV, bullying, and burnout and negatively correlated with job satisfaction (p <0.001). According to the multiple linear regression model, depression was significantly lower among nurses with diploma degrees (β = -1.323, 95% CI = -2.149 to -0.497) and bachelor's degrees (β = -1.327, 95% CI = -2.131 to- 0.523) compared to the nurses with master's degree. The nurses who worked extended hours (>48 hours) had a significantly higher depression score (β = 1.490, 95% CI = 0.511 to 2.470) than those who worked ≤ 36 hours. Depression was found to be significantly higher among those who did not receive a timely salary (β = 2.136, 95% CI = 1.138 to 3.134), rewards for good works (β = 1.862, 95% CI = 1.117 to 2.607), and who had no training on WPV (β = 0.895, 95% CI = 0.092 to 1.698). CONCLUSIONS: Controlling burnout, bullying, and workplace violence, as well as improving the work environment for nurses and increasing job satisfaction, are the essential indicators of reducing depression. This can be accomplished with integrative support from hospital executives, policymakers, and government officials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".