Workplace bullying and turnover intentions among male nurses in Bangladesh: a cross-sectional study
Bibliographic record
Abstract
Background: Turnover among healthcare workers, such as nurses, is an important challenge in the healthcare sector, particularly in developing countries like Bangladesh. Even with the growing level of concern, there is very limited evidence of understanding the association between workplace bullying (WPB) and nurses' turnover intention (TI). Male nurses are a minority in the workplace, and in particular, may face unique societal and workplace challenges in Bangladesh. Thus, this study aimed to investigate the association between WPB and TI as well as identify the factors associated with TI among male nurses in Bangladesh. Methods: In Bangladesh, we conducted a cross-sectional study among nurses between February 2021 and July 2021, and data from 379 registered male nurses were analyzed. The study sites included indoor or outdoor healthcare services. We used the Short Negative Acts Questionnaire-9 to measure WPB and the Turnover Intention Scale-6 to measure TI. We performed a multiple linear regression model to investigate the association between WPB and TI adjusted for the potential covariates. Results: The mean age of the participants was 27 years. Our study found a statistically significant positive association between WPB and TI. Nurses' educational level, smoking status, job types, professional titles, timely payment, and training against violence were also significantly associated with TI. Age, residence, monthly income, accommodation facilities, sufficient equipment, and rewards were not significantly associated with TI. Conclusion: This study found a significant positive association between WPB and TI, and numerous factors were associated with TI among registered male nurses in Bangladesh. Our study emphasizes the need for focused interventions to reduce WPB among male nurses in Bangladesh.
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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 | 0.001 | 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) | 0.002 | 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".