The Career Adapt-Ability Scale (CAAS) is the favored method among researchers for measuring career adaptability. The 12-item version of CAAS-SF, which was made by Maggiori, Rossier, and Savickas based on a change to CAAS, has been slowly used by different groups in different countries and regions. As samples for the validation of the scale in this study, 571 Chinese university graduates in the early stages of their professions were chosen. Principal component analysis and confirmatory factor analysis suggest that CAAS-SF and CAAS have very similar psychological measurement features and factor structures. And the internal consistency of each subscale and total scale are equivalent to or greater than that of the CAAS assessment. These findings indicate that the CAAS-SF is a valid and reliable instrument for evaluating China’s career adaptability. In addition, limitations, issues for further research, and suggestions are emphasized.

Background and Aims. Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of “concentric circles” signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods. A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children’s Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect “concentric circle” signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results. The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the “concentric circle” detection model, the detection rate, recall, specificity, and F 1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by “concentric circle” signs, and the accuracy, recall, specificity, and F 1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion. The model established in this paper can realize the automatic detection of “concentric circle” signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of “concentric circles” in ultrasound images for the judgment of children with intussusception.