Establishment of a risk assessment model for occupational musculoskeletal injuries in clinical nurses

This study aimed to identify occupational, behavioral, and demographic factors influencing occupational musculoskeletal disorders (OMSI) among clinical nurses, establish a cross-sectional risk stratification model, and evaluate its performance in identifying prevalent OMSI status among clinical nurses. In this multicenter cross-sectional study, 300 clinical nurses from six tertiary hospitals in Chongqing were enrolled between May 2023 and May 2024. Participants were allocated to construction ( n = 240) and validation ( n = 60) cohorts (8:2 ratio). Data from validated questionnaires were analyzed using regression methods, and a nomogram-based risk assessment model was developed and evaluated using ROC, calibration, and decision curve analyses. Key occupational, behavioral, and demographic risk-associated factors included age, daily working hours, and use of protective measures, with age showing the highest discriminative ability for OMSI (AUC 0.977), while other variables such as length of service, standing time, and participation in sports provided only modest additional discrimination (AUCs approximately 0.60–0.71; P < 0.05). The nomogram model showed good risk assessment performance (C-index: 0.758; risk threshold > 0.08). The validation group exhibited excellent model accuracy (AUC: 0.994). Calibration and clinical decision curves confirmed the model’s reliability and benefits in practical applications. The developed risk stratification tool demonstrated acceptable performance for cross-sectional OMSI risk identification among clinical nurses. It may provide a preliminary framework for identifying nurses with a higher current probability of OMSI; however, given the cross-sectional design, this tool is intended for screening of current risk states rather than predicting future OMSI incidence. Not applicable.