Positive psychological intervention for anxiety, depression and coping in subjects addicted to online games

The chain mediating role of social support and positive coping between neuroticism and depressive symptoms among graduate students

Introduction

Graduate students face unprecedented levels of neuroticism and pressure compared to their peers. Despite existing research examining the connection between neuroticism and depressive symptoms, a gap in the current understanding of the mediating mechanisms that act on this relationship, especially among this specific student population.

Methods

This study investigated the potential chain-mediating roles of social support and positive coping in the relationship between neuroticism and depressive symptoms among graduate students. The participants were 1845 graduate students who provided demographic information and complete assessments including the Eysenck Personality Questionnaire (EPQ), Depression Rating Scale (BDI-II), Social Support Rating Scale (SSRS), and Coping Style Questionnaire (SCSQ). The analysis involved correlational analysis and a chain mediation model was used to investigate the associations among neuroticism, social support, positive coping mechanisms, and depressive symptoms.

Results

The results show significant correlations among neuroticism, depressive symptoms, social support, and positive coping. Moreover, our findings verify that neuroticism affects depressive symptoms in graduate students through three pathways: the mediating effect of social support, the mediating effect of positive coping, and the chain-mediating effect of social support and positive coping.

Discussion

Overall, the hypothesized chain model conclusively fits the data: Neuroticism directly affects depressive symptoms among graduate students and influences depressive symptoms through the mediating effects of social support and positive coping, as well as the chain mediating effects of these two variables.

The effect on mental well-being, life attitude and depression levels of positive psychology program applied to patients diagnosed with depression

Positive psychology (PP) interventions are treatment methods or intentional activities aiming to develop positive feelings, behaviors or cognitions. This study was conducted to determine the effect on mental well-being, life attitude and depression levels of PP program applied to patients diagnosed with depression. The pre, post and follow-up test were conducted as a quasi-experimental study. Patients who diagnosis of depression for at least 6 months constituted the population of the study, and a total of 53 patients, 26 were in the experimental and 27 in the control group. Data were collected using Personal Information Form,Beck Depression Inventory,Warwick-Edinburgh Mental Well-Being Scale and Life Attitude Profile Scale. After the analysis,it has been determined that the online PP program is effective in reducing the level of depression, increasing mental well-being and developing a positive life attitude. It is recommended that the PP program be used by psychiatric nurses in the treatment of depression and the execution of studies comparing online positive psychology practices with face-to-face positive psychology practices are recommended.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.