Examining Nurses’ Vengeful Behaviors: The Effects of Toxic Leadership and Psychological Well-Being
Bibliographic record
Abstract
Toxic leadership is becoming increasingly common in healthcare organizations and there is strong need for studies focusing on organizational factors that can trigger revenge. Additionally, how psychological well-being functions in shielding against toxicity has not been adequately studied. Hence, this study aims to examine the relationship between toxic leadership and vengeful behaviors of nurses, along with the contingency of psychological well-being on the relationship during the COVID-19 pandemic. In this exploratory cross-sectional study, we attempt to examine the antecedent effect of toxic leadership on vengeful behaviors based on self-reports from 311 nurses. Using partial least squares and moderation analyses, the results show that toxic leadership is an important antecedent of vengeful behaviors among nurses. However, the results provide no statistical evidence to support a moderating role of psychological well-being in the relationship between toxic leadership and vengeful behaviors. This study reveals that nurses exposed to toxic behaviors by their superiors are more likely to engage in vengeance and highlights the fact that nurses are suffering psychologically during the pandemic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".