Development and Validation of a Nomogram for Predicting Presenteeism Risk Among Oncology Nurses: A Cross-sectional Study
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Objectives To develop and validate a predictive nomogram model for assessing the risk of presenteeism among oncology nurses. Background Presenteeism is prevalent among oncology nurses, which affects patient safety and nurse health. However, there is a lack of practical tools to identify individuals at high risk of presenteeism. Methods A cross-sectional study was conducted between May 2025 and September 2025, involving 458 oncology nurses. Participants completed structured questionnaires assessing demographic and occupational factors. A nomogram was constructed to evaluate high presenteeism risk. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis. Results Eight predictors were used to construct the nomogram: nursing experience, hospital level, overtime frequency, monthly income, job satisfaction, psychological capital, perceived social support, and challenge-hindrance stress. The nomogram exhibited strong discrimination, with an AUC of 0.864 (95% CI: 0.824–0.904) in the training set and 0.879 (95% CI: 0.823–0.934) in the validation set. Calibration plots demonstrated good agreement between predicted and observed probabilities (Hosmer–Lemeshow P = 0.915 and 0.243, respectively). Decision curve showed favorable clinical utility across a range of threshold probabilities. Conclusion This nomogram model demonstrates high predictive accuracy and clinical applicability for identifying oncology nurses at high risk of presenteeism. Implications The model offers nursing managers a practical tool for early identification of vulnerable nurses, enabling targeted interventions such as workload adjustments, stress management programs, and social support enhancement to reduce presenteeism and improve both nurse well-being and patient safety.
Source: Research Square
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7212178000