TITLE:
Toxic and Abusive Leadership in Nursing and Workforce Outcomes: A Bayesian Multilevel Meta-Analysis and Meta-Analytic Structural Equation Modeling of Nurse Wellbeing, Retention, and Exploratory Care-Quality Evidence
AUTHORS:
Alice P. Lindström
KEYWORDS:
Toxic Leadership, Abusive Supervision, Nursing Workforce, Turnover Intention, Bayesian Meta-Analysis
JOURNAL NAME:
Open Journal of Nursing,
Vol.16 No.10,
October
20,
2026
ABSTRACT: Background: Toxic and abusive leadership in nursing are measurable organizational exposures, but quantitative evidence remains fragmented across constructs, instruments, outcomes, and settings. Objective: To estimate the magnitude and heterogeneity of associations between toxic or abusive leadership and nursing workforce outcomes and examine modeled indirect pathways to turnover intention. Methods: This PRISMA-guided Bayesian multilevel meta-analysis and meta-analytic structural equation modeling study identified 1,091 records. Twenty-three studies were retained; 20 contributed 48 primary effect sizes and 17 contributed 39 correlations to MASEM. Correlations were harmonized so positive values indicated more adverse outcomes and transformed using Fisher’s (z). Robustness was assessed using posterior predictive checks, alternative priors, and sensitivity analyses. Results: Toxic or abusive leadership was associated with poorer wellbeing (r = 0.34; 95% CrI, 0.23 - 0.45) and retention-related outcomes (r = 0.33; 95% CrI, 0.19 - 0.46). Prediction intervals were wide. The just-identified MASEM showed a direct modeled association with turnover intention (0.311; 95% CI, 0.239 - 0.383) and an indirect association through psychological distress (0.035; 95% CI, 0.015 - 0.056). Twenty-two of 23 studies had serious overall risk of bias; care-quality evidence comprised one effect. Conclusions: Toxic and abusive leadership are associated with poorer nurse wellbeing and retention-related outcomes. Structural pathways are associational rather than causal, and care-quality evidence remains exploratory.