Christina Parpoula

As internet use among adolescents increases, internet addiction has become a crucial public mental health issue, putting adolescents at risk of psychological distress. Research indicates a bidirectional association between internet addiction and adolescents’ development of depressive symptoms. Although current evidence shows that anxiety can predict internet addiction, which subsequently predicts depression, the underlying mechanisms of the relationship between internet addiction and mental distress remain unclear, while the role of age differences in internet addiction warrant further research. The present cross-sectional study explores how internet addiction and stress mediate the relationship between anxiety and depression, and how the association between depression and internet addiction varies with the developmental stage of adolescence. A convenient sample of 215 Greek adolescents, aged from 11 to 20 years, were sourced from internet forums, including social media, and completed the Internet Addiction Test (IAT-20) and the Depression, Anxiety and Stress Scale-21 (DASS-21). Middle stage adolescents (ages 15-17), showed increased levels of internet addiction (Mdn = 52) compared to early (Mdn = 44) and late (Mdn = 49.5) stage adolescents. The results from path analyses revealed that internet addiction and stress serially mediate the relationship between anxiety and depression. Furthermore, depression was found to be moderated by the stage of adolescence with the strongest relationship observed in early adolescence (ages 11-14). These findings contribute to a more thorough understanding of how internet addiction is related to psychological distress among adolescents that may assist clinicians with designing age-specific interventions to address both internet addiction and psychological distress.

The recent advances in technological capabilities have led to a massive production of time‐series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time‐series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time‐series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population‐ and individual‐level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real‐time monitoring tool of time‐series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

During the COVID-19 pandemic, the Greek population was already burdened by the strain of a decade-long financial crisis, which had caused life disruption, insecurity, uncertainty, and loss of symbolic capital, inevitably placing a huge cumulative burden on their mental health. Since other discernable population-level changes in mental health have not yet emerged, continued monitoring is warranted. Identifying emerging needs for mental health services was the major objective of the present study. To accomplish this, a multiple change-point detection and validation approach was employed to analyze Google Trends data, exploring community interest in mental health therapists and treatment practices in Greece. Specifically, Google search data from pre- and post-financial and pandemic crises periods (2004-2023) were analyzed to assess the search frequency of specific topics/terms of interest. The results revealed that, over the last six years or so, searches for terms like “psychologist”, “psychiatrist”, “psychotherapist”, and “neurologist”, as well as treatment practices such as “psychotherapy”, “meditation”, and “antidepressants”, have increased. However, a decline in searches for “mental health counselor”, “psychotherapy”, “psychoanalysis”, “meditation” and “mental health helplines” was observed after 2011 (persisting for at least the next 5-7 years in certain cases). Searches for “psychoanalyst”, “mental health centre” and “anxiolytics” remained relatively stable throughout the study period. These results, combined with ongoing surveillance, can offer insights into public interest in mental health services and guide public mental health initiatives to be better tailored to the needs of communities, ultimately addressing the psychological toll of the preceding socioeconomic crisis and the current COVID-19 situation.

The impact of COVID-19 and the associated lockdown measures on people’s physical and mental wellbeing, as well as their daily lives and functioning, has been extensively studied. This study takes the approach of investigating the consequences of COVID-19 on a national scale, considering sociodemographic factors. The main objective is to make a contribution to ongoing research by specifically examining how age, gender, and marital status influence the overall impact of COVID-19 and wellbeing indicators during the second lockdown period that was implemented in response to the COVID-19 pandemic in the Greek population. The study involved a sample of 16,906 individuals of all age groups in Greece who completed an online questionnaire encompassing measurements related to personal wellbeing, the presence and search for meaning in life, positive relationships, as well as symptoms of depression, anxiety, and stress. Additionally, to gauge the levels of the perceived COVID-19-related impact, a valid and reliable scale was developed. The results reveal that a higher perception of COVID-19 consequences is positively associated with psychological symptoms and the search for meaning in life, while being negatively correlated with personal wellbeing and the sense of meaning in life. In terms of individual differences, the findings indicate that unmarried individuals, young adults, and females tend to report higher levels of psychological symptoms, a greater search for meaning in life, and a heightened perception of COVID-19-related impact. These findings are analyzed in depth, and suggestions for potential directions for future research are put forth.

Artificial intelligence has evolved enormously over the last two decades, becoming mainstream in different scientific domains including education, where so far, it is mainly utilized to enhance administrative and intelligent tutoring systems’ services and academic support. ChatGPT, an artificial intelligence-based chatbot, developed by OpenAI and released in November 2022, has rapidly gained attention from the entire international community for its impressive performance in generating comprehensive, systematic, and informative human-like responses to user input through natural language processing. Inevitably, it has also rapidly posed several challenges, opportunities, and potential issues and concerns raised regarding its use across various scientific disciplines. This paper aims to discuss the legal and ethical implications arising from this new technology, identify potential use cases, and enrich our understanding of Generative AI, such as ChatGPT, and its capabilities in education. The paper is part of the Proceedings of the 1st International Conference of the Network of Learning and Teaching Centers in Greece: Transforming Higher Education Teaching Practice

Artificial intelligence has evolved enormously over the last two decades, becoming mainstream in different scientific domains including education, where so far, it is mainly utilized to enhance administrative and intelligent tutoring systems services and academic support. ChatGPT, an artificial intelligence-based chatbot, developed by OpenAI and released in November 2022, has rapidly gained attention from the entire international community for its impressive performance in generating comprehensive, systematic, and informative human-like responses to user input through natural language processing. Inevitably, it has also rapidly posed several challenges, opportunities, and potential issues and concerns raised regarding its use across various scientific disciplines. This paper aims to discuss the legal and ethical implications arising from this new technology, identify potential use cases, and enrich our understanding of Generative AI, such as ChatGPT, and its capabilities in education.

Change-point analysis is the task of finding abrupt (and significant) changes in the underlying model of a signal or time series. Change-point detection methods typically involve specifying the maximum number of segments to search for and the minimum segment length. However, there is no objective way to pre-specify these two parameters, and it mostly depends upon the particular application. Within this framework, a recursive optimization algorithm is developed that is capable of exploring and fine tuning these two input parameters, and optimally segmenting a time series. This multiple change-point detection technique therefore addresses a wide class of real-life contexts and problems where the identification of optimal level shifts in a time series is the main goal. Extensive simulation results are presented and a real-life example is given to illustrate the implementation of the developed scheme in practice and to unfold its capabilities. Concluding remarks and suggestions for future research are also provided.

Worldwide, the detection of epidemics has been recognized as a continuing problem of crucial importance to public health surveillance. Various approaches for detecting and quantifying epidemics of infectious diseases in the recent literature are directly influenced by methods of Statistical Process Control (SPC). However, implementing SPC quality tools directly to the general health care monitoring problem, in a similar manner as in industrial quality control, is not feasible since many assumptions such as stationarity, known asymptotic distribution etc. are not met. Toward this end, in this paper, some of the open statistical research issues involved in this field are discussed, and a distribution-free control charting technique based on change-point analysis is applied and evaluated for detection of epidemics. The main tool in this methodology is the detection of unusual trends, in the sense that the beginning of an unusual trend marks a switch from a control state to an epidemic state. The in-control and out-of-control performance of the adapted control scheme from SPC is thoroughly investigated using Monte Carlo simulations, and the applied scheme is found to outperform its parametric and nonparametric competitors in many cases. Moreover, the empirical comparative study provides evidence that the adapted change-point detection scheme has several appealing properties compared to the current practice for detection of epidemics.