Workplace bullying and turnover intention among male nurses: A cross-sectional study in Bangladesh
Bibliographic record
Abstract
Abstract Background: Workplace bullying (WPB) and nurses’ turnover intention (TI) are important challenges in the healthcare sector, particularly in developing countries like Bangladesh. Understanding this relationship is crucial for developing targeted interventions to improve retention and well-being among male nurses in Bangladesh. Thus, this study aimed to explore the relationship between WPB and TI among Bangladeshi male nurses. Method: We conducted a cross-sectional study among 379 Bangladeshi registered male nurses between April 26 and July 10, 2021. The study sites included indoor or outdoor settings where nurses provide healthcare. We used the Short Negative Acts Questionnaire-9 (S-NAQ-9) to measure WPB and the Turnover Intention Scale-6 (TIS-6) to assess TI. We performed a multiple linear regression model to explore the association of WPB and other variables with TI. Results: The study participants were predominantly young male nurses, with a significant proportion employed in urban settings and holding a Bachelor of Science (B.Sc.) degree. The study found a significant positive association between WPB and TI, suggesting that higher levels of WPB were related to increased TI. Likewise, some other factors such as educational degree, smoking status, job types, professional titles, timely payment, and violence-related training showed significant associations with TI. Conclusion: This study highlights the need for focused interventions to reduce WPB and enhance working conditions for male nurses in Bangladesh. Addressing WPB, as well as improving work satisfaction through targeted initiatives, is critical for reducing TI among this demographic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".