RETRACTED: Impact of workplace bullying and burnout on job satisfaction among Bangladeshi nurses: A cross-sectional study
Post-publication record
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Bibliographic record
Abstract
Background: Job satisfaction is one of the most important but least researched issues in the nursing profession in Bangladesh. This study aimed to investigate how workplace bullying and burnout are related to job satisfaction, as well as determine the factors that are associated with job satisfaction among Bangladeshi nurses. Methods: Data were collected from Bangladeshi registered nurses between February 26, 2021, and July 10, 2021, in this cross-sectional study. Bullying, burnout, and job satisfaction were measured with the Short Negative Acts Questionnaire [S-NAQ], the Burnout Measure-Short version (BMS), and the Short Index of Job Satisfaction (SIJS-5), respectively. The correlations between age, bullying, burnout, and job satisfaction were assessed using a Pearson's correlation test. In order to investigate the adjusted association of demographic characteristics, occupational variables, bullying, and burnout with job satisfaction, multiple linear regression models were fitted. Results: The study included 1,264 nurses (70.02% were female) with a mean age of 28.41 (±5.54) years. Job satisfaction was significantly negatively correlated with bullying and burnout (p < 0.001). According to the multiple linear regression models, the private-employed nurses had lower job satisfaction than the government-employed nurses (β = -0.901, CI: -1.640 to -0.162). Compared to the nurses in the Dhaka division, the nurses in the Chattogram division (β = 0.854, CI: 0.099 to 1.609) and other divisions (β = 0.993, CI: 0.273 to 1.713) had higher job satisfaction. Nurses without sufficient equipment to manage patients (β = -1.230, CI: -1.696 to -0.763), and nurses not paid on time (β = -1.475, CI: -2.221 to -0.729) were predicted to have significantly lower job satisfaction. Nurses' job satisfaction levels were decreased with higher levels of workplace bullying (β = -0.086, CI: -0.120 to -0.053), and burnout (β = -1.040, CI: -1.242 to -0.838). Conclusions: Nurses' job satisfaction was correlated with workplace bullying and burnout. Moreover, insufficient professional support from the authorities predicted nurses' job satisfaction. Reducing the instances of bullying and burnout among nurses, as well as improving their working environment, are essential to increase job satisfaction. This is possible with the support of hospital management, policymakers, and government authorities.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".