Objective Explore effective strategies for hospitals to manage the National Natural Science Foundation of China.Methods A retrospective analysis was conducted on the population characteristics of a certain tertiary comprehensive hospital's National Natural Science Foundation project application and approval from 2016 to 2022.Single factor analysis and bi-nary multivariate logistic regression analysis were used to analyze the factors that affect the approval of the National Natural Sci-ence Foundation project,and a function prediction model was established.Results A total of 1 098 applicants were collected in this study,including 653 young project applicants and 178 approved,445 general project applicants and 114 approved;Multi fac-tor logistic results show that in youth programs,the younger the age,the doctor's degree,scientific research posts,the experi-ence of studying abroad,the average impact factor of publishing SCI papers as the first/corresponding author is 3-5 points Appli-cants who have published SCI papers as the first/corresponding author with an average impact factor of more than 5 points,pub-lished SCI papers in four districts as the first/corresponding author,presided over national level and academic level topics are more likely to be approved by the Youth Program of the National Natural Science Foundation of China;Among general programs,applicants who have doctoral degree,overseas study experience,published Zone 1and Zone 2 SCI essays,and presided over na-tional projects as the first/corresponding author are more likely to be approved as general programs of the National Natural Science Foundation of China.The area under the AUC curve of the youth project regression prediction model is 0.860,and the area under the AUC curve of the surface project regression prediction model is 0.838.The model has high predictive value.Conclusion Hospitals should strengthen policy guidance,increase economic investment,encourage applicants to carry out preliminary work layout,focus on talent cultivation,and comprehensively promote the effective management of the National Natural Science Foun-dation of the hospital.

Objective:To construct a scientific research evaluation model through principal component analysis, and to explore scientific research evaluation methods for hospitals.Methods:The professional title, educational background, positions and scientific research output information of the scientific research personnel in the First Hospital of Jilin University from 2019 to 2020 were collected. Delphi expert consultation was used to determine the assignment value of each variable, and use SPSS 21.0 software was used to build the principal component analysis model and conduct model verification.Results:The study collected a total of 1 882 researchers′ information. The KMO value of the validity test and the Bartlett sphere test meet the requirements of principal component analysis (KMO=0.731, P<0.05); the model obtained a total of 7 principal components. Among them, principal component 1 represents researchers who published SCI papers, applying for national, provincial and ministerial level scientific research projects, and their part-time positions in academic societies. The second principal component represents the status of applying for patents and publications, and the third principal component represents the status of the awards. The scores of scientific research output of researchers were summarized and sorted according to disciplines, according to which the neurology, endocrinology and metabolism, neurosurgery, general surgery and orthopedics ranked better. The model verification results found that researchers with senior professional titles and doctoral degrees had the highest median weighted comprehensive score( P<0.05), suggesting that scholars with higher professional title levels and higher education received higher comprehensive scientific research output scores. Conclusions:The scientific research evaluation model constructed by this study can provide scientific data reference for the hospital scientific research evaluation.