Technology and Anxiety

Climate Anxiety and Nature Connectedness

IntroductionClimate change is a source of global concern that has both direct and general impacts on mental health. A recent study conducted following severe bushfires in Australia demonstrated relationships among nature connectedness, climate action, climate worry, and mental health; for example, nature connectedness was associated with climate worry, which in turn was associated with psychological distress.MethodsThe present study sought to replicate those findings while building on them in two important ways: on those findings in two ways: first, test similar relationships in a different geographical context that has been mostly spared from direct impacts by acute climate events; second, we take into consideration an additional factor, climate knowledge, which has been linked to relevant factors such as climate anxiety.ResultsThe results of a survey completed by 327 adults revealed a similar relationship between nature connectedness and climate anxiety, and between that and psychological distress. Further mirroring those previous findings, nature connectedness was associated with both individual and collective climate action, but the relationships between them and psychological distress differed.DiscussionThe proposed model was a better fit to the collected data among those with high levels of climate change knowledge than those with low levels, suggesting that such knowledge influences how the above factors relate to each other.

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Network analysis of the relationships between problematic smartphone use and anxiety, and depression in a sample of Chinese college students

… the aim of this study was to closely examine the relationships between [problematic smartphone use] (PSU) and anxiety and depression to identify the pathological mechanisms underpinning those relationships. A second aim was to identify important bridge nodes to identify potential targets for intervention. … … Five strongest edges appeared within the communities in both

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A New Model Predicts Depression and Anxiety Using Artificial Intelligence and Social Media

Utilizing data from Twitter and applying natural language processing artificial intelligence algorithms, researchers created a new, accurate prediction model for depression and anxiety.

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Social media use and adolescents’ well-being: A note on flourishing

BackgroundSeveral large-scale studies and reviews have reported both negative and positive associations of social media use with well-being, suggesting that the findings are more complex and need more nuanced study. Moreover, there is little or no exploration of how social media use in adolescence influences flourishing, a more all-encompassing construct beyond well-being, including six sub-domains (i.e., happiness, meaning and purpose, physical and mental health, character, close social relationships, and financial stability). This paper aims to fill this gap by understanding how adolescents might flourish through social media activities by fulfilling the basic needs pointed out by the Self-Determination Theory, i.e., relatedness, autonomy, and competence.MethodsThe study is drawn on cross-sectional data collected from 1,429 Swiss adolescents (58.8% females, Mage = 15.84, SDage = 0.83) as part of the HappyB project in Spring 2022. Self-reported measures included the Harvard Adolescent Flourishing scale, positive and negative online social experiences, self-disclosure on social media, and social media inspiration. Control variables included, among others, self-esteem, ill-being, and personality.ResultsAfter applying Bonferroni’s correction, results of the hierarchical regression analyses showed that positive social media experiences (β = 0.112, p 

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Elon Musk among tech bosses and experts in call for 6-month pause on artificial intelligence systems amid ChatGPT fears

Elon Musk is among a group of technology sector experts and top industry executives calling for a six-month pause in developing artificial intelligence (AI) systems more powerful than new versions of ChatGPT.

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Association between mental health symptoms and behavioral performance in younger vs. older online workers

The COVID-19 pandemic has been associated with increased rates of mental health problems, particularly in younger people. We quantified mental health of online workers before and during the COVID-19 pandemic, and cognition during the early stages of the pandemic in 2020. A pre-registered data analysis plan was completed, testing the following three hypotheses: reward-related behaviors

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Remote working and occupational stress: Effects on IT-enabled industry employees in Hyderabad Metro, India

In the present study, the researchers reported the results of an empirical study on remote working and occupational stress and their effects on employees’ job satisfaction, motivation, and performance. Remote working has three subscales: self-proficiency, technology, and teamwork. Intrinsic and extrinsic motivation subscales were included to assess employee motivation. A simple random sampling method was used to select the subjects who are employees of the IT-enabled industries in Hyderabad Metro. A total of 513 responses were obtained on the remote working subscales—the effect on the independent variables, namely, employee self-proficiency, technology, teamwork, and occupational stress, on the dependent variables, namely, job satisfaction, intrinsic motivation, extrinsic motivation, and performance. The measured Cronbach’s alpha was in the range of 0.64–0.77, other reliability statistics split-half (odd-even) correlation was in the range of 0.62–0.84, and theSpearman–Brown prophecy was in the range of 0.70–0.91, demonstrating the reliability and internal consistency of the research instrument. The general linear model results indicated that all the independent variables, namely, self-proficiency, teamwork, and Occupational stress, are statistically significant and influence the outcome variables. The general linear model results also indicated statistically significant age differences in the dependent variables; however, there were no statistically significant gender differences. Of the independent variables, self-proficiency influences job satisfaction, intrinsic motivation, and performance (p 

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