Objective To construct a framework and relevant evaluation items for evaluating the core competence of clinical nursing teachers. Methods We explored the whole frame of competence structure and established behavior items for evaluating competences with corresponding level-marking standards through theory analysis, literature study, expert consultation and a small sample test. We also investigated 237 nursing managers, teachers, and students from six medical schools in China using a 5-point Likert-type scale. Item analysis and factor analysis were performed to reveal competence construction and connotation, and the evaluation model for core competence of clinical nursing teachers were constructed. Results Content, structure and internal consistency coefficient were checked for validity. Cronbach＇s alpha of the total system was 0. 9109 and of each domain ranged from 0. 6064 to 0. 8474. Results of Split-half Reliability demonstrated that of total system was 0. 8765 and of each domain ranged from 0. 634 2 to 0. 8369. Conclusions The evaluation model for core competence of clinical nursing teachers is composed of three parts： competence domains, behavior items and their corresponding grade standards. Core competence of clinical nursing teachers consist of four domains：leadership ability, problem-solving ability, teaching ability and nursing ability, each of which contains several behavior items and corresponding four grades standards reflecting different abilities.
Objective To explore factors affecting the shunt safety of patients in emergency intensive care unit (EICU), construct a shunt safety evaluation model, and evaluate its prediction effectiveness, so as to provide a theoretical basis for the decision-making of shunt safety in EICU. Methods The demographic data, vital signs, laboratory examinations and other indicators of patients transferred to the general ward from the EICU of West China Hospital of Sichuan University from 0:00 on August 1, 2019 to 23:59 on May 31, 2021 were collected and analyzed. The short-term poor prognosis after being transferred out of the EICU was regarded as the end-point event. Of the patients, 70% were randomly selected as the model construction cohort, and 30% were the model validation cohort. In the model construction cohort, multivariate logistic regression analysis was used to screen the influencing factors affecting shunt safety, and the shunt safety evaluation model of patients in EICU was constructed. In the validation cohort, receiver operating characteristic curve was used to evaluate the effectiveness of the model in evaluating the shunt safety of patients in EICU. Results A total of 582 patients were included, of whom 59 patients (10.1%) had a poor short-term prognosis. Multivariate logistic regression analysis showed that the patients’ respiratory rate when leaving the EICU [odds ratio (OR)=0.863, 95% confidence interval (CI) (0.794, 0.938), P=0.001], Glasgow Coma Scale scores [OR=1.575, 95%CI (1.348, 1.841), P<0.001], albumin [OR=1.137, 95%CI (1.008, 1.282), P=0.036], prothrombin time [OR=0.956, 95%CI (0.914, 1.000), P=0.048] were the influencing factors of shunt safety. Based on the above indicators, a shunt safety evaluation model for patients in EICU was created. The area under the curve for the shunt safety assessment model to predict poor short-term prognosis was 0.815, the best cut-off value was 4 points, the sensitivity was 93.3%, and the specificity was 61.5%. Conclusions The patients’ respiratory rate when leaving EICU, Glasgow Coma Scale scores, albumin and prothrombin time are factors affecting the shunt safety for patients in EICU. The shunt safety assessment model can better predict the short-term poor prognosis of patients transferred from EICU to general ward.
Our team proposed and constructed an Expert-knowledge and Data-driven Comprehensive Evaluation Model of Chinese Patent Medicine (EDCEM-CPM) using the machine learning algorithm. This model could improve the system of the comprehensive evaluation of the Chinese patent medicine in technology and provide measurement tools for Chinese patent medicine according to its characteristics. The model evaluates the multi-dimensional value of Chinese patent medicine by data pre-treatment, clustering algorithms, and data training steps, such as automatic learning weighting. This evaluation model is already in practice. In this paper, we introduced the establishment of the model with the calculation process for reference.