When workload predicts exposure to bullying behaviours in nurses: The protective role of social support and job recognition
Bibliographic record
Abstract
AIMS: This study examined the moderating role of two resources (social support and recognition) in the longitudinal relationship between workload and bullying behaviours in nurses. DESIGN: A two-wave (12-month) longitudinal study was conducted. METHOD: French-Canadian nurses (n = 279) completed an online survey (October 2014 and October 2015) assessing their perceptions of job characteristics within the work environment (workload, social support, job recognition) as well as exposure to negative behaviours at work. RESULTS: Workload positively predicted exposure to bullying behaviours over time, but only when job recognition and social support were low. Workload was unrelated to bullying when social support was high and was negatively related to bullying when job recognition was high. CONCLUSION: This study aligns with the work environment hypothesis, showing that poorly designed and stressful job environments provide fertile ground for bullying behaviours. IMPACT: Bullying is a growing concern in the nursing profession that not only undermines nurses' well-being but also compromises patient safety and care. It is thus important to identify work-related factors that can contribute to the presence of bullying behaviours in nurses in the hopes of reducing their occurrence and repercussions. This study contributes to this endeavour and identifies two key social coping resources that can help manage the stress associated with workload, resulting in less perceived bullying behaviour among nurses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".