ObjectiveTo explore the relationship between the core self-evaluations,copying style and learning engagement of college students.Methods 250 college students were asked to complete the core self-evaluations scale,copying style scale and learning engagement scale.ResultsBivariate correlations results indicated that there was significantly positive correlation between the core self-evaluations,copying style and learning engagement in college students( r=0.179 ～ 0.586,P＜ 0.05 ) ; core self-evaluations affected motivation indirectly by the copying style; core self-evaluations had a direct influence on vigor and absorption; core self-evaluations had a indirect influence on vigor and absorption.ConclusionCopying style was a full mediator of the relationship of the core self-evaluations and motivation,but copying style partially mediated the relationship of the core self-evaluations and vigor and absorption.

Objective:The study seeks to explore the factors influencing the psychological status and sleep quality of medical workers amid the ongoing COVID-19 pandemic, in order to provide data sources and theoretical basis for the development of relevant psychological intervention programs.Methods:Employing the convenience sampling method, general information questionnaire (age, gender, marital status, educational background, job status, etc.), Generalized Anxiety Disorder-7 and Patient Health Questionnaire, epidemic stress index scale, and sleep quality questionnaire were distributed to medical staff between February 18 and April 3, 2020, using the PEM mental health care platform of by ZhongShengKaiXin for medical staff issued. Descriptive, single factor, and correlation analyses, as well as multiple linear regression analysis were used to analyze the data.Results:Overall, 24, 845 questionnaires were collected from 23 provinces, of which 24, 687 were valid, with a recovery rate of 99.36%. The findings showed that the proportion of medical personnel with symptoms of anxiety and depression was 50.58% and 51.37%, respectively; 16.11% had poor or very poor anti-stress ability; and 71.78% reported poor or very poor sleep quality. There was a positive correlation between anxiety, depression, anti-stress ability, and sleep quality ( P<0.05). Anxiety was positively correlated with depression, stress tolerance, and sleep quality( r=0.787, 0.667, and 0.486, all P<0.001); depression was positively correlated with stress tolerance and sleep quality ( r=0.709 and 0.586, both P<0.001); and stress tolerance was positively correlated with sleep quality ( r=0.452, P<0.001). Multiple linear regression analysis results showed that age, gender, marital status, educational background, professional title, job status, and participation influenced the anxiety levels of medical personnel in the backdrop of the pandemic ( P<0.001). Depression levels of medical staff were influenced by gender, educational background, job position, and participation ( P<0.001), while gender, marital status, educational background, job position, and participation influenced the stress tolerance levels ( P<0.001). The sleep quality of medical workers was influenced by age, gender, job position, participation in the fight against the pandemic, and professional title ( P<0.001). Conclusions:Amid the ongoing COVID-19 pandemic, medical staff reported poor mental health status and sleep quality, which can be attributed to diverse factors. The research findings can be useful for assisting medical staff to strengthen their self-cognition, while also providing certain psychological counseling data and theoretical basis for management departments.

OBJECTIVES: From the point of view of clinical data representation, this study attempted to identify obstacles in translating clinical narrative guidelines into computer interpretable format and integrating the guidelines with data in Electronic Health Records in China. METHODS: Based on SAGE and K4CARE formulism, a Chinese clinical practice guideline for hypertension was modeled in Protege by building an ontology that had three components: flowchart, node, and vMR. Meanwhile, data items imperative in Electronic Health Records for patients with hypertension were reviewed and compared with those from the ontology so as to identify conflicts and gaps between. RESULTS: A set of flowcharts was built. A flowchart comprises three kinds of node: State, Decision, and Act, each has a set of attributes, including data input/output that exports data items, which then were specified following ClinicalStatement of HL7 vMR. A total of 140 data items were extracted from the ontology. In modeling the guideline, some narratives were found too inexplicit to formulate, and encoding data was quite difficult. Additionally, it was found in the healthcare records that there were 8 data items left out, and 10 data items defined differently compared to the extracted data items. CONCLUSIONS: The obstacles in modeling a clinical guideline and integrating with data in Electronic Health Records include narrative ambiguity of the guideline, gaps and inconsistencies in representing some data items between the guideline and the patient' records, and unavailability of a unified medical coding system. Therefore, collaborations among various participants in developing guidelines and Electronic Health Record specifications is needed in China.
Asian Continental Ancestry Group ; China* ; Clinical Coding ; Cooperative Behavior ; Decision Support Systems, Clinical ; Delivery of Health Care ; Electronic Health Records ; Humans ; Hypertension ; Methods* ; Practice Guidelines as Topic ; Software Design ; Translating