Loneliness and cognitive distortions have serious negative effects on life satisfaction and are seen as related to each other. However, what mediates in these relationships has not been adequately analyzed. This study aims to analyze the effect between cognitive distortions and life satisfaction by asserting the mediating role of loneliness. For this purpose, we conducted a cross-sectional study with 978 people over the age of 20 from various parts of Turkey using a general screening model. Firstly, we analyzed the direct effect of interpersonal rejection, interpersonal misperception, and unrealistic relationship expectation, which are sub-dimensions of cognitive distortions, loneliness, and life satisfaction. Secondly, we analyzed the effects of independent, mediation, and interaction (Loneliness X age) variables on satisfaction with life. Thirdly, we applied direct regression analysis between independent, mediating, and dependent variables to conduct the mediation analysis of the study. Also, we examined age as a moderator between loneliness and life satisfaction. The results showed loneliness as an essential determinant between cognitive distortions and life satisfaction. Moreover, in the moderation analysis, we found some age-related differences between loneliness and life satisfaction. According to this, both loneliness and life satisfaction increased with increasing age. However, life satisfaction decreased as loneliness increased in young people. Being able to compensate for loneliness in the aging process is an essential psychological indicator. Although young people have more opportunities than older people, their ability to cope with negative situations such as loneliness is largely undeveloped. These results showed both psychological and sociological problems of loneliness. Therefore, the results can be useful for the prevention of loneliness and the development of intervention programs.

This study examined the pathways through which learning orientation and performance orientation impact life satisfaction by investigating the mediating role of problematic social media use and the moderating influence of school type. The study recruited 443 participants aged between 13 and 31 (M = 18.41 ± 3.134), comprising 224 high school students and 219 university students, through an online survey. Data collection instruments included the Achievement Goal Questionnaire, Social Media Addiction Scale, and Life Satisfaction Scale. Results revealed that learning orientation influenced life satisfaction indirectly through problematic social media use, whereas performance orientation had a direct impact on life satisfaction. Moderation analyses revealed a significant decrease in life satisfaction for high school students compared to university students as problematic social media use increased. These findings highlight the importance of implementing tailored interventions based on achievement orientations and life satisfaction to minimize issues arising from excessive social media use by students.

Aim: This study examined the mediating roles of depression and various dimensions of loneliness (family, social, and romantic) in the associations between COVID‐19 fear and satisfaction with life. Additionally, the research investigated how the economic impact of COVID‐19, family income, gender, and marital status may moderate these associations.Design: A cross‐sectional descriptive design.Methods: An online survey was conducted in Türkiye. 1702 participants completed self‐report measures assessing COVID‐19 fear, depression, social, emotional, and romantic loneliness, and satisfaction with life.Results: The findings revealed positive associations between COVID‐19 fear and depression, along with negative relationships between COVID‐19 fear and family, social, and romantic loneliness. Depression was identified as a mediator in the relationship between COVID‐19 fear and the different components of loneliness. These loneliness components, in turn, influenced life satisfaction. Furthermore, the study found significant moderation effects related to the economic impact of COVID‐19 restrictions, family income, marital status, and gender.Conclusions: This study provides evidence about the psychological mechanisms linking COVID‐19 fear to life satisfaction. It highlights the mediating role of depression and the moderating effects of demographic and economic factors. The findings emphasise the need for targeted mental health interventions, particularly addressing the varied impact of COVID‐19 fear across different social and economic groups. This evidence can inform strategies aimed at improving life satisfaction during periods of social and economic disruption.Patient or Public Contribution: This study has no direct patient involvement in its design, conduct, or reporting. However, it contributes to public health by providing evidence about the psychological impacts of COVID‐19, which may inform future interventions and mental health support strategies.

This study aims to evaluate the usability and reliability of ChatGPT, a large language model, to conduct Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis. For this purpose, a research model based on the Technology Acceptance Model was tested with 523 participants using both SmartPLS and ChatGPT. Key statistical indicators such as internal consistency, convergent and discriminant validity, VIF values, path coefficients, and R-square values were compared. The results show a high level of consistency between ChatGPT and SmartPLS outputs throughout all stages of the analysis. Furthermore, the statistical validity of SmartPLS and ChatGPT outputs is supported by examining their Pearson Correlation (r), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) values. Both tools identified the same significant and nonsignificant relationships, and similar mediation effects were found. Although ChatGPT lacks visualization capabilities and requires step-by-step guidance, it successfully reproduced statistically valid results. These results suggest that ChatGPT may be a complementary and cost-effective tool for researchers, especially those without access to licensed software. Future research should explore its usability with other statistical techniques and artificial intelligence models.