Objective To analyze the influencing factors of disaster resilience in medical rescue workers,to construct a prediction model for the low-level risk of disaster resilience in medical rescue workers,and to verify the predictive effect of the model.Methods Using the convenience sampling method and the snowball method,1 037 medical rescue workers who participated in disaster rescue in 18 provinces(autonomous regions and municipalities)were selected as the participants from May to July 2022.Online questionnaire surveys were conducted using general information questionnaires,disaster resilience measuring tools for healthcare rescuers,the Mindful Attention Awareness Scale,the Simple Coping Style Questionnaire and the Depression-Anxiety-Stress Scale.Univariate and multivariate logistic regression analyses were used to determine the independent influencing factors for the low level of disaster resilience of medical rescue workers.A risk prediction model was constructed,and a nomogram chart was drawn.The model's effectiveness was evaluated using the receiver operating characteristic curve(ROC)and calibration curve.The Bootstrap method was applied for internal validation.Results The logistic regression analysis showed that per capita monthly income of households,whether to participate in on-site disaster rescue,positive coping,mindfulness level,and adequacy of rescue supplies were independent influencing factors for the disaster resilience of medical rescue workers(P＜0.05).The predictive formula for the low-level risk of disaster resilience in medical rescue workers was established as follows:Logit(P)=8.741-0.381 x per capita monthly income of households-0.891 x whether to participate in on-site disaster rescue-2.544 x positive coping-0.020 x mindfulness level-0.222 x adequacy of rescue supplies.The area under the ROC curve was 0.823,and the optimal critical value was 0.353.The sensitivity and specificity were 79.12％and 71.43％,respectively.The Hosmer-Lemeshow test showed that x2=12.250(P=0.140),and the predicted curve fitted well with the ideal curve.The external validation showed that the sensitivity and specificity of the model were 75.00％and 66.39％,respectively,and the overall accuracy was 69.95％.Conclusion The prediction model in this study has sound predictive effects and can provide references and guidance for managers to select,recruit,and train medical rescue workers.