RESUMEN
The detection rate of mental health problems among undergraduates has recently risen significantly. However, undergraduates underutilize mental health services; approximately a third only of undergraduates in need of treatment use school counseling resources. Based on a social psychological theoretical framework, the health belief model, factors of undergraduates' willingness to seek help when dealing with psychological problems were investigated. A cross-sectional online questionnaire and a snowball sampling method with 446 undergraduates investigated perceived susceptibility, perceived severity, perceived behavioral benefits, perceived barriers, self-efficacy, and cues to action to understand how students' mental health-seeking behaviors are affected. We found that perceived susceptibility (p < 0.01), perceived severity (p < 0.01), perceived benefits (p < 0.01), perceived barriers (p < 0.01), self-efficacy (p < 0.01), and cues to action (p < 0.01) significantly correlated with behavioral intention. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help. In addition, we used a bias-corrected Bootstrap approach to test the significance of the mediating effect, the mediation effect of cues to action between undergraduates' perceived susceptibility and mental health help-seeking behavior was utterly significant [mediation effect value of 0.077, with an SE value of 0.027 and a 95% CI (0.028, 0.133)]. It demonstrated that those who perceived themselves to be at high risk of developing a mental illness and who had received encouragement or counseling to seek mental health help were more likely to be motivated to seek mental health help. Multiple regression analyses indicated that self-efficacy (Z = 5.425, p < 0.01) and cues to action (Z = 6.673, p < 0.01) independently influenced behavioral intentions. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help.
RESUMEN
The dissemination of public opinion in the social media network is driven by public sentiment, which can be used to promote the effective resolution of social incidents. However, public sentiments for incidents are often affected by environmental factors such as geography, politics, and ideology, which increases the complexity of the sentiment acquisition task. Therefore, a hierarchical mechanism is designed to reduce complexity and utilize processing at multiple phases to improve practicality. Through serial processing between different phases, the task of public sentiment acquisition can be decomposed into two subtasks, which are the classification of report text to locate incidents and sentiment analysis of individuals' reviews. Performance has been improved through improvements to the model structure, such as embedding tables and gating mechanisms. That being said, the traditional centralized structure model is not only easy to form model silos in the process of performing tasks but also faces security risks. In this article, a novel distributed deep learning model called isomerism learning based on blockchain is proposed to address these challenges, the trusted collaboration between models can be realized through parallel training. In addition, for the problem of text heterogeneity, we also designed a method to measure the objectivity of events to dynamically assign the weights of models to improve aggregation efficiency. Extensive experiments demonstrate that the proposed method can effectively improve performance and outperform the state-of-the-art methods significantly.
