Clinicians and supervisors' experiences with developing practices of open dialogue in network meetings in public mental health services

Open Dialogue is a dialogical approach focusing on the perspectives of patients and their social networks on treatment and recovery processes. As part of a larger research project, this prospective cohort study explores what promotes and hinders the development of Open Dialogue in network meetings (ODNM) based on the experiences of thirty-seven clinicians and seven supervisors. Multistage focus group interviews were used to collect data and were analysed thematically. We generated two main themes: (1) togetherness and isolation and (2) challenging and evolving. The findings show that ODNM can be developed in public mental healthcare, but this leads to both challenges and opportunities at the organisational level, such as conflicting perspectives, the difficulty of maintaining interest in ODNM, the need for committed and involved leaders, and the growing change in the traditional view of treatment, which has made clinicians collaborate more with patients and their relatives.     

The organization of construal networks and functional adaptation

This review examined the association of construal network organizations with functional adaptation and psychological well-being. Recent neuropsychological research supports the presence of distinct construal networks in the brain that organize action at different levels of goals and tasks. Construal networks are sets of connected construals, or mental representations of objects, events, and behaviors. Little attention, however, has been given to how the organization of construal networks promotes functional adaptation. Cognitive processes, construal levels, personal meaning, cultures, and situations influence the configurations of construal networks. The reviewed evidence indicated that construal network organization facilitates functional adaptation and well-being, either though the coherence or fit of the assembled construals with each other or through the mediation of their fit with situations or contexts, like a culture. This review goes beyond previous studies by describing the constructive, creative, and hypothetical aspects of construal organizations and their effects on functional adaptation and psychological well-being.     

Acute exercise and children’s cognitive functioning: What is the optimal dose of cognitive challenge?

Acute bouts of exercise have the potential to benefit children’s cognition. Inconsistent evidence on the role of qualitative exercise task characteristics calls for further investigation of the cognitive challenge level in exercise. Thus, the study aim was to investigate which “dose” of cognitive challenge in acute exercise benefits children’s cognition, also exploring the moderating role of individual characteristics. In a within-subject experimental design, 103 children (M_age = 11.1, SD = 0.9, 48% female) participated weekly in one of three 15-min exergames followed by an Attention Network task. Exergame sessions were designed to keep physical intensity constant (65% HR_max) and to have different cognitive challenge levels (low, mid, high; adapted to the ongoing individual performance). ANOVAs performed on variables that reflect the individual functioning of attention networks revealed a significant effect of cognitive challenge on executive control efficiency (reaction time performances; p = .014, ƞ²_p = .08), with better performances after the high-challenge condition compared to lower ones (ps < .015), whereas alerting and orienting were unaffected by cognitive challenge (ps > .05). ANOVAs performed on variables that reflect the interactive functioning of attention networks revealed that biological sex moderated cognitive challenge effects. For males only, the cognitive challenge level influenced the interactive functioning of executive control and orienting networks (p = .004; ƞ²_p = .07). Results suggest that an individualized and adaptive cognitively high-challenging bout of exercise is more beneficial to children’s executive control than less challenging ones. For males, the cognitive challenge in an acute bout seems beneficial to maintain executive control efficiency also when spatial attention resources cannot be validly allocated in advance. Results are interpreted referring to the cognitive stimulation hypothesis and arousal theory.     

Dynamics and moderating factors of esport participation and loneliness: A daily diary study

The purpose of this study was to examine the association between esport participation and loneliness, as well as its moderating factors. Chinese college students (N = 216) self-reported their esport playing time and degree of loneliness each day immediately before bedtime for four consecutive weeks. The findings revealed that as playing time increased, students did not experience reduced sense of loneliness during playing esport, but they experienced a temporary and intensified feeling of loneliness the same day following gameplay. Students with higher general loneliness tended to feel more after-game loneliness associated with increased esport participation. Students with higher obsessive passion about esport tended to experience more loneliness (both in-game and after-game) associated with increased esport participation. Students with greater coping motivation about esport tended to experience more in-game loneliness associated with previous increased esport participation. Students who played esport less often with friends in person, or with more escape motivation toward esport, tended to increase esport participation time more following previous increased after-game loneliness. The findings suggested that college students should avoid utilizing esport to achieve a sense of belonging and should be cautious of the loneliness-inducing effect after gameplay. A healthy level of loneliness can be maintained by playing esport more with friends in person, learning strategies to avoid obsessive passion, coping motivation, and escape motivation towards esport.     

Eating Disorder Symptom and Fear Change Trajectories During Imaginal Exposure Therapy: A Slope Network Analysis

Eating disorders (EDs) are characterized by fears related to food, body image, and social evaluation. Exposure-based interventions hold promise for targeting a range of ED fears and reducing ED psychopathology. We investigated change mechanisms and optimal fear targets in imaginal exposure therapy for EDs using a novel approach to network analysis. Individuals with an ED (N = 143) completed up to four online imaginal exposure sessions. Participants reported ED symptoms and fears at pretreatment, posttreatment, and 6-month follow-up. We constructed networks of symptoms (Model 1), fears (Model 2), and combined symptoms and fears (Model 3). Change trajectory networks from the slopes of symptoms/fears across timepoints were estimated to identify how change in specific ED symptoms/fears related to change in other ED symptoms/fears. The most central changing symptoms and fears were feeling fat, fear of weight gain, guilt about one’s weight/shape, and feared concerns about consequences of eating. In Model 3, change in ED fears bridged to change in desire to lose weight, desiring a flat stomach, following food rules, concern about eating with others, and guilt. As slope networks present averages of symptom/fear change slopes over the course of imaginal exposure therapy, further studies are needed to examine causal relationships between symptom changes and heterogeneity of change trajectories. Fears of weight gain and consequences of eating may be optimal targets for ED exposure therapy, as changes in these fears were associated with maximal change in ED pathology. Slope networks may elucidate change mechanisms for EDs and other psychiatric illnesses.     

Towards end-to-end likelihood-free inference with convolutional neural networks

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Once trained on simulated data, the convolutional neural network is able to map real data samples of variable size to the first two posterior moments of the relevant parameter's distributions. Thus, in contrast to other machine learning approaches to ABC, our approach allows us to generate reusable models that can be applied by different researchers employing the same model. We verify the utility of our method on two common statistical models (i.e., a multivariate normal distribution and a multiple regression scenario), for which the posterior parameter distributions can be derived analytically. We then apply our method to recover the parameters of the leaky competing accumulator (LCA) model and we reference our results to the current state-of-the-art technique, which is the probability density estimation (PDA). Results show that our method exhibits a lower approximation error compared with other machine learning approaches to ABC. It also performs similarly to PDA in recovering the parameters of the LCA model.     

A Social Network Analysis of Friendship and Spiritual Support in a Religious Congregation

Religious congregations are social settings where people gather together in community to pursue the sacred (Pargament, 2008). Such settings are important to understand as they provide a context for individuals to develop relationships, share ideas and resources, and connect individuals to larger society (Todd, 2017a). Yet, research to date has not deeply examined the inherently relational nature of religious congregations. Thus, in this study, we used social settings theory (Seidman, 2012; Tseng & Seidman, 2007) to develop and test hypotheses about relationships within one Christian religious congregation. In particular, we used social network analysis to test hypotheses about relational activity, popularity, and homophily for friendship and spiritual support types of relational links. Our findings demonstrate how relational patterns may be linked to participation in congregational activities, occupying a leadership role, a sense of community and spiritual satisfaction, stratification, socialization, and spiritual support. Overall, this advances theory and research on the relational aspects of religious congregations, and more broadly to the literature on social settings. Limitations, directions for future research, and implications for theory and religious congregations also are discussed.     

Finding a home: Developmental and neurobiological perspectives on cultural models

This article looks at cultural models in the light of human development, and neurobiological findings in motivation, learning, and cognition. It is argued that at the individual level, the acquisition of cultural models relies on several innate, neurobiologically based motivational, learning, and cognitive systems. These are: (a) a primary motivation to form social bonds which is driven by affect; (b) highly specialized social learning circuits, involving, but not limited to, mirror neuron systems, that facilitate the encoding of social information through implicit, embodied, imitational learning processes; and (c) the formation of culturally based templates for behavior and cognition centered around structures, collectively known as the “default mode network,” which is essential to self‐understanding, autobiographical memory, social cognition, prospection, and theory‐of‐mind. Cultural models, it is argued, are acquired through innate motivational processes that tie the individual emotionally to a secure base of familiar people and customs. This instinctual desire for proximity to others facilitates the efficient, largely implicit, patterning of knowledge and expectations. Shared knowledge and expectations, in turn, create a common, mostly implicit or unconscious, experience of subjectivity within groups. This allows each individual to automatically and effortlessly interact with similarly enculturated others.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.     

Characterizing positive and negative ‘voicers’ in elite sport teams: The role of the Five Factor Model and narcissism in players’ frequency and passing on of voice

ObjectiveThis study examined the role of the Five Factor Model and grandiose narcissism in players’ positive (i.e., constructive voice, supportive voice) and negative voice (i.e., defensive voice, destructive voice) in elite sport teams.MethodPlayers from six field hockey and seven korfball teams from the two highest national levels were assessed for four weeks. Using social network analyses, players’ personality was related to their self-reported voice frequency, their voice frequency as perceived by all teammates (other-ratings), and the extent to which they pass on voice.ResultsExtraversion was positively related to players’ frequency of positive and negative voice. Other traits such as conscientiousness and emotional stability were only related to, respectively, positive or negative types of voice. Not all personalities (e.g., extraversion) were consistent in how they assess their own voice versus how others perceive this. Interestingly, traits such as extraversion, emotional stability and the agentic facet of narcissism were found to predict the passing on of voice.ConclusionThis study explored the importance of personality for (a) players’ frequency of a differentiated set of positive and negative voice and (b) the extent to which they function as ‘gates’ that more covertly pass on voice. Further, the results provide perspective on how specific personalities view their voice behavior versus how their teammates perceive their voice behavior. In this way, this study is a first step in identifying players who have the potential to endanger or strengthen a team in a clear or subtle, yet influential way.