Background: Mental health is a public health problem of great concern. Previous studies show that textual features and individual psychological characteristics can influence the effect of receiving information.
Purpose: This study explores whether textual features influence the persuasiveness of teenager students' mental health education while considering the influence of risk preference.
Methods: From November to December 2021, a cross-sectional study was conducted among 1,869 teenager students in grade 7-12 in Chongqing, China. Wilcoxon signed-rank test, multiple logistic regression, and subgroup analysis were used to analyze the data.
Results: Among the four textual features mentioned in this study, a significant difference was reported in the persuasive effects of information with and without numerical features ( < 0.001), and such information tended to include digital features. The result for the symbolic features ( < 0.001) was consistent with the numerical features. The persuasive effects of positive and negative emotional information significantly differed ( < 0.001), with the former showing a better performance. No significant differences were observed between the persuasive effects of information with and without emotional conflicts ( > 0.05). Combined with those from the risk preference analysis, results showed that the regulatory effect of risk preference was only reflected in emotional conflicts. Students who prefer having no emotional conflict in the text showed the characteristics of risk avoidance, or lower grades, or rural or school accommodation. Most teenager students are also risk averse, especially females (or = 2.223, 95%CI:1.755-2.815) and juniors (or = 1.533, 95%CI: 1.198-1.963).
Conclusion: The numbers, symbols, and positive emotions in the text generate an active effect on teenager students receiving mental health education. Students avoiding risk are inclined to read texts without emotional conflicts. The probability of male choosing texts with positive emotional polarity is 33.5% lower than that of female. Female students and those from lower grades also demonstrate a higher inclination to risk avoidance compared with their male and higher grade counterparts. Therefore, educational materials with different text characteristics should be developed for teenager students with varying characteristics.
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http://dx.doi.org/10.3389/fpsyg.2022.911955 | DOI Listing |
Sci Rep
December 2024
Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.
The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Academic institutions face increasing challenges in predicting student enrollment and managing retention. A comprehensive strategy is required to track student progress, predict future course demand, and prevent student churn across various disciplines. Institutions need an effective method to predict student enrollment while addressing potential churn.
View Article and Find Full Text PDFNpj Ment Health Res
December 2024
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany.
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model.
View Article and Find Full Text PDFbioRxiv
December 2024
Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
Artificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM).
View Article and Find Full Text PDFNeural Netw
December 2024
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Beijing Key Laboratory of Network System and Network Culture, Beijing, China.
The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions. A primary challenge in this task is bridging the substantial representational gap between visual and textual modalities. The prevailing methods map texts and images into unified embedding space for matching, while the intricate semantic correspondences between texts and images are still not effectively constructed.
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