Aims To explore the status quo and the influencing factors of residents’ knowledge, attitude and practice (KAP) in the prevention and control of coronavirus disease 2019 (COVID‐19), and the difficulties or challenges perceived by residents in their preventive practice. Design An online questionnaire survey. Methods The self‐designed questionnaire was distributed among residents online in February 2020. Descriptive statistics, two independent samples t ‐tests, one‐way analysis of variance, Pearson's correlation analysis, multivariate linear regression and content analysis were performed. Results A total of 919 valid questionnaires were collected. The scoring rates of residents’ KAP were 85.2%, 92.9% and 84.4% respectively. Main factors influencing residents’ knowledge included gender and occupation; while those influencing attitude were occupation, family economic level and knowledge; and those influencing practice included place of residence, occupation, with or without chronic disease, knowledge and attitude. Mass media was the primary approach for people to learn the knowledge and information of COVID‐19. Difficulties or challenges faced were mainly lack of protective equipments, concerns about the risk of prevention and control, impact on daily life, work and study, lack of knowledge and consensus, psychological problems and information problems. Conclusion The attitude of residents towards COVID‐19 prevention and control is generally positive. The knowledge and practice have been popularized to a certain extent, but there are still deviations or deficiencies in residents’ understanding of certain important knowledge and the adoption of relevant preventive measures. Evidence‐based tailored public education initiatives are indicated. Impact Findings of this study add important knowledge about residents’ understanding, attitude, practice and the influencing factors on COVID‐19 prevention and control, which serves as a scientific foundation for optimizing the pandemic public education and decision‐making.

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Online mental health service (OMHS) platforms have contributed significantly to the public’s mental health during the COVID-19 pandemic in China. However, it remains unclear why the public used OMHS platforms for psychological help-seeking (PHS) behavior and how PHS behavior varied across different stages of the COVID-19 pandemic. Based on the ecological PHS behavior data from two OMHS platforms, we extracted population, psychological problems, and influential factors of PHS behavior by text mining and time series analysis methods. Seven top-ranked psychological problems (i.e., depression and anxiety, lack of interest, suicidal tendencies, social phobia, feelings of being worried and afraid, suffering, anger) and seven influential factors (i.e., interpersonal relationships, love, family, work, psychotherapy, personal characteristics, marriage) were found. The online PHS behaviors related to different psychological problems and influential factors remained a growing trend before 2020 and have been increasing significantly due to the COVID-19 outbreak. Four main stages were found during the pandemic according to the changes in the online PHS population: sharp growth, significant decline, slight rebound, and slow decline. This study identified large-scale, spontaneous PHS behaviors among the online public during the COVID-19 pandemic and the various psychological problems and influential factors that varied across different stages of the pandemic, suggesting that the government and health practitioners should adopt effective policies and strategies to prevent and intervene in mental health problems for the online public.

Online lifestyles have been shown to reflect and affect consumers’ preferences across a wide range of online scenarios. In the context of e-commerce, it still remains unclear whether online lifestyles are practically influential in predicting consumers’ purchasing preferences across different product categories, especially considering its potential influence over the widely used personality traits. In this study, we provide the first, to the best of our knowledge, quantitative demonstration of online lifestyles in predicting consumers’ online purchasing preferences in e-commerce by using a data-driven approach. We first construct an online lifestyles lexicon including seven distinct dimensions using text mining approaches based on consumers’ language use behaviors. We then incorporate the lexicon in a typical e-commerce recommender system to predict consumers’ purchasing preferences. Experimental results on Amazon Review Dataset show that online lifestyles and all its subdimensions significantly improve preference predicting performance and outperform the widely used Big Five personality traits as a whole. In addition, product types significantly moderate the influence of online lifestyle on consumer preference. The strong empirical evidence indicates that the big e-commerce consumer data facilitates more specialized market psychographic segmentation, which advances data-driven marketing decision-making.

Globally, colon adenocarcinoma (COAD) is one of the most frequent types of malignant tumors. About 40~50% of patients with advanced colon adenocarcinoma die from recurrence and metastasis. Long non-coding RNAs (lncRNAs) and 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of COAD. The goal of this study was to explore the biological characteristics and potential predictive value of 5mC-related lncRNA signature in COAD. In this research, The Cancer Genome Atlas (TCGA) was utilized to obtain the expression of genes and somatic mutations in COAD, and Pearson correlation analysis was used to select lncRNAs involved in 5mC-regulated genes. Furthermore, we applied univariate Cox regression and Lasso Cox regression to construct 5mC-related lncRNA signature. Then Kaplan–Meier survival analysis, principal components analysis (PCA), receiver operating characteristic (ROC) curve, and a nomogram were performed to estimate the prognostic effect of the risk signature. GSEA was utilized to predict downstream access of the risk signature. Finally, the immune characteristics and immunotherapeutic signatures targeting this risk signature were analyzed. In the results, we obtained 1652 5mC-related lncRNAs by Pearson correlation analysis in the TCGA database. Next, we selected a risk signature that comprised 4 5mC-related lncRNAs by univariate and Lasso Cox regression. The prognostic value of the risk signature was proven. Finally, the biological mechanism and potential immunotherapeutic response of the risk signature were identified. Collectively, we constructed the 5mC-related lncRNA risk signature, which could provide a novel prognostic prediction of COAD patients.

Analyzing people's opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users' real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users' real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users' online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users' real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users' real mood states and could be applied in further psychological measurements and predictions.

BackgroundLittle research has assessed the degree of severity and ordering of different types of sexual behaviors for HIV/STI infection in a measurement scale. The purpose of this study was to apply the Rasch model on psychometric assessment of an HIV/STI sexual risk scale among men who have sex with men (MSM).MethodsA cross-sectional study using respondent driven sampling was conducted among 351 MSM in Shenzhen, China. The Rasch model was used to examine the psychometric properties of an HIV/STI sexual risk scale including nine types of sexual behaviors.ResultsThe Rasch analysis of the nine items met the unidimensionality and local independence assumption. Although the person reliability was low at 0.35, the item reliability was high at 0.99. The fit statistics provided acceptable infit and outfit values. Item difficulty invariance analysis showed that the item estimates of the risk behavior items were invariant (within error).ConclusionsThe findings suggest that the Rasch model can be utilized for measuring the level of sexual risk for HIV/STI infection as a single latent construct and for establishing the relative degree of severity of each type of sexual behavior in HIV/STI transmission and acquisition among MSM. The measurement scale provides a useful measurement tool to inform, design and evaluate behavioral interventions for HIV/STI infection among MSM.

Despite the great attention paid to Internet literacy research, little has been done to overcome the problems stemming from the heterogeneity of Internet literacy nomenclature and the use of non-standardized measurement tools, especially for adolescents in developing countries. Considering junior students are the high-risk groups of Internet addiction and have wide access to the Internet, the aim of this study is to develop a new scale to assess Chinese junior students’ Internet literacy (JIL). In the psychometric study (n = 1099 junior students), an 18-item scale was developed using the exploratory and confirmatory factor analyses, which includes five subscales: knowledge and skills for the Internet (KSI), Internet self-management (ISM), awareness and cognition of Internet (ACI), Internet interactions (II), and autonomous learning on the Internet (ALI). Evidence of internal reliability, test-retest reliability, and construct validity provided good psychometric support for the measure. Criterion-related validity of the measures was demonstrated by examining its anticipated theoretical relations to two hypotheses: (1) High JIL level alleviates the adverse effects of an individual’s Internet addiction degree, while pathological use for interacting with others on the Internet exacerbates the adverse effects; (2) an individual’s degree of Internet use self-efficacy is positively associated with JIL level. It is envisaged that the JIL Scale will help facilitate unified research in the field.