Network structures of internet gaming disorder and gaming disorder: Symptom operationalization causes variation.

OBJECTIVE: From 2022, the International Classification of Diseases, eleventh edition (ICD-11) includes the first mental disorder based on digital technology, "gaming disorder," which was previously suggested as a condition for further examination in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5). In this cross-sectional study, we provide the first large-scale network analysis of various symptom structures for these constructs to understand the complex interconnections between their proposed symptoms. METHOD: Culturally diverse samples of 2,846 digital game players (M = 25.3 years) and 746 esports players (M = 23.5 years) were recruited. A network approach was applied to explore a multiverse of gaming disorder symptom structures, effects of item operationalization, and possible external moderators. Gaming disorder was measured using the Internet Gaming Disorder Scale 9-Short Form (IGDS9-SF), Gaming Disorder Test, and several items borrowed from Chinese Internet Gaming Disorder Scale, Personal Internet Gaming Disorder Evaluation-9, and Clinical Video game Addiction Test 2.0 scales. RESULTS: Two symptoms (loss of control and continued use despite problems) present in both, the DSM-5 and ICD-11, were systematically central to most of the analyzed networks. Alternative operationalizations of single items systematically caused significant network differences. Networks were invariant across groups of play style, age, gender, gaming time, and most of the psychosocial characteristics. CONCLUSIONS: Our results caution practitioners and researchers when studying and interpreting gaming disorder symptoms. The data indicate that even minor operational changes in symptoms can lead to significant network-level changes, thus highlighting the need for careful wording. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Perception of Hate Speech by the Public and Experts: Insights into Predictors of the Perceived Hate Speech Towards Migrants.

Online hate speech is a matter of concern for social media platforms, regulators, researchers, and the public. Despite its widespread prevalence and contentious nature, little research has been done on the perception of hate speech and its psychosocial predictors. To address this gap, we conducted a study on the perception of hate speech toward migrants in online comments, analyzing the differences between a public group (NPublic = 649) and an expert group (NExperts = 27) and exploring the correlation between the proposed hate speech indicators and perceived hate speech in both groups. Additionally, we explored various predictors of hate speech perception, including demographic and psychological variables such as human values, prejudice, aggression, impulsiveness, social media behavior, attitudes toward migrants and migration, and trust in institutions. Our results show that the public and experts have differing sensitivities toward hate speech, with the expert group perceiving comments as more hateful and emotionally harmful compared with the general population, who tend to agree more with antimigrant hateful comments. The proposed hate speech indicators and especially their total scores have a strong correlation with both groups' perceptions of hate speech. Psychological predictors, such as the human values of universalism, tradition, security, and subjective social distance, were significant predictors of online hate speech sensitivity. Our findings emphasize the need for public and scholarly discussions, more robust educational policies, and intervention programs with specific measures to counter hate speech online.

Risk and protective factors for (internet) gaming disorder: A meta-analysis of pre-COVID studies.

This large-scale meta-analysis aimed to provide the most comprehensive synthesis to date of the available evidence from the pre-COVID period on risk and protective factors for (internet) gaming disorder (as defined in the DSM-5 or ICD-11) across all studied populations. The risk/protective factors included demographic characteristics, psychological, psychopathological, social, and gaming-related factors. In total, we have included 1,586 effects from 253 different studies, summarizing data from 210,557 participants. Apart from estimating these predictive associations and relevant moderating effects, we implemented state-of-the-art adjustments for publication bias, psychometric artifacts, and other forms of bias arising from the publication process. Additionally, we carried out an in-depth assessment of the quality of underlying evidence by examining indications of selective reporting, statistical inconsistencies, the typical power of utilized study designs to detect theoretically relevant effects, and performed various sensitivity analyses. The available evidence suggests the existence of numerous moderately strong and highly heterogeneous risk factors (e.g., male gender, depression, impulsivity, anxiety, stress, gaming time, escape motivation, or excessive use of social networks) but only a few empirically robust protective factors (self-esteem, intelligence, life satisfaction, and education; all having markedly smaller effect sizes). We discuss the theoretical implications of our results for prominent theoretical models of gaming disorder and for the existing and future prevention strategies. The impact of various examined biasing factors on the available evidence seemed to be modest, yet we identified shortcomings in the measurement and reporting practices.

Measuring Internet Gaming Disorder and Gaming Disorder: A Qualitative Content Validity Analysis of Validated Scales.

Numerous instruments have been developed to measure gaming-related health problems based on "internet gaming disorder" (IGD) in the third section of the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) and "gaming disorder" (GD) in the International Classification of Diseases (11th rev.). However, the criteria in the manuals tend to be operationalized in numerous diverse ways, which can make screening outcomes incomparable. A content validity analysis is needed to reassess the relationships between the diagnostic criteria and the items that operationalize them. The IGD and GD criteria were divided into sematic components. A qualitative content validity analysis was carried out for all items employed by the 17 instruments that claim to measure either construct by their criteria in English. In all but one instrument, the operationalizations did not include all criterion components. There were two main reasons found for this: the components had simply been left out or had been alternatively modified into other components. Criteria that were vaguely described in the manuals were sources of lower content validity items. The study implies that many of the problems in IGD and GD measurement derive from criteria operationalization and original manual descriptions. The conclusion provides practical recommendations that researchers can apply to improve the content validity of their measurement.

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

A Review of Consequences of Poverty on Economic Decision-Making: A Hypothesized Model of a Cognitive Mechanism.

This review focuses on the issue of poverty affecting economic decision-making. By critically evaluating existing studies, the authors propose a structural model detailing the cognitive mechanism involved in how poverty negatively impacts economic decision-making, and explores evidence supporting the basis for the formation of this model. The suggested mechanism consists of a relationship between poverty and four other factors: (1) cognitive load (e.g., experiencing negative affect and stress); (2) executive functions (e.g., attention, working memory, and self-control); (3) intuition/deliberation in decision-making; and (4) economic decision-making (e.g., time-discounting and risk preference), with a final addition of financial literacy as a covariate. This paper focuses on shortfalls in published research, and delves further into the proposed model.