The Media and Teenage Violence: How Much Is Too Much When It Comes to Adolescent Aggression?

With the advent of increasingly accessible technology and the growing prevalence of both fictional and nonfictional violence in the media (American Academy of Pediatrics 2009; American Psychological Association 2013; Emmons 2013; Pew Research Center 2015), the effects of such content on emotional states and behavioral patterns in youths has garnered a great deal of interest among sociologists, psychologists, and developmental experts. However, the results of existing research are inconclusive and often contradictory (APA 2013; Siegel and Welsh 2017:86-87), providing no clear answer to the question of whether or not this content actually affects viewer behavior. I explore the current body of literature examining the relationship between exposure to media violence and its elicitation of aggression and violence in children, adolescents, and young adults before performing my own analysis of data from a previously completed study (Schneider and Waite 1998-2000), specifically analyzing variables that have not yet been examined together. Finally, I suggest directions for future research, discuss relevant limitations, and offer conclusions based on the past and present results

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. // Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. // Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. // Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. // Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging

Combined effects of occupational noise exposure and shiftwork on performance tasks in a seafaring population

This study was undertaken to complement a cross-sectional survey of the combined effects of selfreported workplace stressors. Data were collected from seafarers on board support vessels for the North Sea oilrigs as part of a project on offshore fatigue. These data could also be used specifically to address whether there were any cognitive effects associated with working in loud noise at night that were different to working in loud noise during the day, low noise at night or low noise during the day. The participants were 62 male workers from 3 different vessels. Their mean age was 40.3 years. Individuals were from a range of different jobs onboard the vessels. There were two between-subjects factors (day/night shift and noise exposure) and one within-subjects factor (test session). Workers were asked to complete a battery of computer tests both before (Pre-shift) and after (Post-shift) their shift on one day. Four tests were presented using laptop computers. These tests were visual analogue mood scales, a simple variable fore-period reaction time, and categoric search and focused attention choice reaction time tasks. The mood scales were presented at the beginning and end of the testing session. Occupational noise exposure (Leq) was measured over a two-day period using a dosimeter. Workers were categorised into day/night workers by their shift pattern. Regression analyses distinguishing noise exposure, day/night shift and their interaction were performed on the data from each test session and the change score between the start and end of the shift. Noise exposure was associated with increased alertness but also with slower reaction times. Those working night shifts showed a large drop in alertness over the course of work and became slower at tasks requiring more difficult responses. There were also a limited number of interactions between noise and shift, such as more lapses of attention (very long response times) but fewer incorrect responses in the noise/night-work condition. The findings suggest that these techniques may provide valuable information about the possible combined effects of occupational stressors in situ. The present analyses are based only on a small number of night workers and further consideration of effects of potential confounding influences must also be undertake

Attachment problems in childhood and the development of anxiety in adolescents: A systematic review of longitudinal and prospective studies

© 2019 Elsevier GmbH The association between early relationships and the experience of infant and mother, and the subsequent development of the child through their life span has long been studied. Attachment, as a most prominent construct in early child development, has been the focal point of investigation since the inception of the theoretical concept by Bowlby. In recent years, research has expanded to examine the effect of attachment on the mental health and socio-emotional development of young children and their on-going adjustment into adolescence. However, most studies in the field concentrated on the relationship of childhood attachment and internalising symptoms as a generic assessment of mental health problems. To provide more precise information on the effect of attachment insecurity on individual mental health problems, a systematic review (the first in a series) of available longitudinal and prospective studies was conducted. 11 studies were identified after an extensive search of the literature in accordance to the PRISMA guidelines. Of these, 4 satisfied all selection criteria and provided sufficient data on the effect of attachment insecurity during infancy or early childhood and anxiety in adolescence. Information was extracted and analysed systematically from each study and tabulated. The overall results obtained from these studies indicated a significant and possible causal relationship between attachment insecurity during infancy or early childhood and the development of anxiety in adolescence. These results were discussed in light of theoretical and practical preventive implications

The Costs of Climate Change: A Study of Cholera in Tanzania

Increased temperatures and changes in rainfall patterns as a result of climate change are widely recognized to entail potentially serious consequences for human health, including an increased risk of diarrheal diseases. This study integrates historical data on temperature and rainfall with the burden of disease from cholera in Tanzania and uses socioeconomic data to control for the impacts of general development on the risk of cholera. The results show a significant relationship between temperature and the incidence of cholera. For a 1 degree Celsius temperature increase the initial relative risk of cholera increases by 15 to 29 percent. Based on the modeling results, we project the number and costs of additional cases of cholera that can be attributed to climate change by 2030 in Tanzania for a 1 and 2 degree increase in temperatures, respectively. The total costs of cholera attributable to climate change are shown to be in the range of 0.32 to 1.4 percent of GDP in Tanzania 2030. The results provide useful insights into national-level estimates of the implications of climate change on the health sector and offer information which can feed into both national and international debates on financing and planning adaptation

Transmission and diffusion:Linguistic change in the regional French of Béarn

ABSTRACTThis article examines the seemingly dichotomous linguistic processes of transmission and diffusion (Labov, 2007) in the regional variety of French spoken in Béarn, southwestern France. Using a sociophonetic apparent time methodology, an analysis of nasal vowel quality provides evidence for the advancement of linguistic changes from below taking place between successive generations during the transmission process, as well as for change from above taking place in the variety as a result of exposure to diffusing non-local varieties of French. The results address Labov's (2007) assertion that it is rare to investigate incremental changes occurring from below in European dialectological studies and shed light on the transmission–diffusion interface by showing the adoption of an individual change from above to instigate a faithfully-transmitted counterclockwise chain shift in the regional French nasal vowel system.</jats:p

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach

BACKGROUND: The aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk. METHODS: We acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms. RESULTS: In the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04). CONCLUSIONS: The severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults

Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging?

Introduction High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points. Methods A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multisite study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels. Results Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas. Conclusions The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points. Copyright: &copy; 2016 Portugal et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach

BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses. METHODS: The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels. RESULTS: GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions. CONCLUSIONS: These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology