Association of workplace bullying and burnout with nurses’ suicidal ideation in Bangladesh
Bibliographic record
Abstract
Suicidal ideation is a complex phenomenon influenced by several predisposing, contextual, and mediating factors that seem more common among healthcare workers, especially nurses. We investigated the association of bullying and burnout with suicidal ideation among Bangladeshi nurses and identified the associated factors. We conducted a cross-sectional study among 1264 nurses in Bangladesh between February 2021 and July 2021. We applied a modified Poisson regression model with robust error variance to determine the association of bullying and burnout with suicidal ideation. Among 1264 nurses, the female was 882 (70.02%), and the mean age was 28.41 (SD = 5.54) years. The prevalence of high levels of suicidal ideation was 13.26%. In the Poison regression model, high risk bullying (RR = 6.22, 95% CI 3.13-12.38), targeted to bullying (RR = 7.61, 95% CI 3.53-16.38), and burnout (RR = 8.95, 95% CI 2.84-28.20) were found to be significantly associated with suicidal ideation. Furthermore, we found significant interaction between workplace bullying and burnout with suicidal ideation (p < 0.05). The high prevalence of bullying, burnout, suicidal ideation, and their association indicate an unsafe workplace for the nurses. Initiatives are needed to make a favorable work environment to improve nurses' overall mental health and reduce suicide ideation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".