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Despite recent growth of research on the effects of prosocial media, processes underlying these effects are not well understood. Two studies explored theoretically relevant mediators and moderators of the effects of prosocial media on helping. Study 1 examined associations among prosocial- and violent-media use, empathy, and helping in samples from seven countries. Prosocial-media use was positively associated with helping. This effect was mediated by empathy and was similar across cultures. Study 2 explored longitudinal relations among prosocial-video-game use, violent-video-game use, empathy, and helping in a large sample of Singaporean children and adolescents measured three times across 2 years. Path analyses showed significant longitudinal effects of prosocial- and violent-video-game use on prosocial behavior through empathy. Latent-growth-curve modeling for the 2-year period revealed that change in video-game use significantly affected change in helping, and that this relationship was mediated by change in empathy.

ABSTRACT Objective: to assess the accuracy of Brøset violence checklist in predicting violence in psychiatric inpatients in a Pakistani setup. Study Design: Cross-sectional study. Place and Duration of Study: Department of Psychiatry, Mayo Hospital, Lahore Pakistan, from Mar to Sep 2021. Methodology: One Hundred and Sixty-Seven patients from either gender, admitted to an acute psychiatric ward, were included in the study and their scoring was done. Socio-demographic information was collected from patients' files, and violence data and preventive measures were recorded on the Staff Observation Aggression Scale-Revised form by nursing staff for three days. SOAS-R score of 9 or more was declared as a violent incident. Results: Of the one hundred and sixty-seven patients, the mean age was 34.48±10.06 years. 109(65.3%) were males, and 58(34.7%)were females. Forty-nine episodes of violence were recorded. At a cutt off point of 2, Brøset violence checklist Sensitivity and Specificity were 69.4% and 92.4%, respectively. The corresponding PPV and NPV values were 79.1% and 92.4 %, respectively. ROC characteristics yielded an area under the curve of 0.86, showing good predictive accuracy. Conclusion: Brøset violence checklist has good predictive accuracy for the prediction of imminent violence in a psychiatric setting in Pakistan.

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.

The correct version of the table is below. Description of sample: diagnosis and outcomes variables (n = 36,293) Diagnosis Serious legal trouble past 12 m Lifetime incarceration Lifetime violence to others n 12m n life n (%) OR (95% CI) c n (%) OR (95% CI) c n (%) OR (95% CI) c No diagnosis 22618 17775 154 (0.7%) Ref 809 (4.6%) Ref 1660 (9.4%) Ref Any SU, MI or PD 13675 18518 502 (3.7%) 6.1*** (5.1; 7.4) 3321 (18.0%) 4.7*** (4.2; 5.2) 6547 (35.4%) 4.9*** (4.5; 5.3) Any SU or MI 12126 17755 467 (3.9%) 6.4*** (5.3; 7.7) 3181 (18%) 4.7*** (4.2; 5.2) 6117 (34.5%) 4.7*** (4.3; 5.1) Any MI or PD 10797 13843 394 (3.6%) 6.2*** (5.2; 7.5) 2402 (17.4%) 4.5*** (4.0; 5.1) 5419 (39.2%) 5.8*** (5.3; 6.2) Any SU or PD 9514 13219 462 (4.9%) 8.3*** (6.9; 10.0) 3034 (23.1%) 6.3*** (5.6; 7.1) 5681 (43.1%) 6.6*** (6.1; 7.2) Comorbid SU and MI 2214 5470 171 (7.7%) 14.0*** (11.3; 17.4) 1428 (26.2%) 7.4*** (6.6; 8.4) 2743 (50.2%) 8.8*** (8.1; 9.7) Comorbid SU and PD 2039 3455 205 (10.1%) 19.5*** (15.7; 24.2) 1287 (37.4%) 13.2*** (11.5; 15.2) 2522 (73.0%) 24.3*** (21.5; 27.5) Comorbid MI and PD 3480 4198 207 (5.9%) 10.6*** (8.5; 13.3) 1186 (28.3%) 8.9*** (7.8; 10.3) 1665 (20.6%) 17.3*** (15.4; 19.4) Comorbid SU and PD and MI 1323 2671 132 (10.0%) 19.0*** (14.6; 24.6) 963 (36.1%) 12.4*** (10.7; 14.5) 1944 (72.8%) 23.7*** (20.8; 26.9) Any MI 8532 12296 286 (3.4%) 5.6*** (4.6; 6.9) 1938 (15.8%) 4.0*** (3.6; 4.5) 4411 (35.9%) 5.0*** (4.6; 5.4) Schizophrenia/Psychosis 337 902 28 (8.3%) 17.5*** (10.6; 28.9) 218 (24.2%) 6.5*** (4.9; 8.6) 381 (42.2%) 6.9*** (5.3; 9.0) Any Mood Disorder 4894 8639 198 (4.0%) 7.1*** (5.7; 8.9) 1382 (16.1%) 4.1*** (3.6; 4.6) 3252 (37.7%) 5.4*** (4.9; 5.8) Major Depression 3961 7430 149 (3.8%) 6.6*** (5.1; 8.4) 1061 (14.4%) 3.5*** (3.1; 4.0) 2584 (34.8%) 4.7*** (4.3; 5.2) Persistent Depressive 1185 2017 52 (4.4%) 7.6*** (5.4; 10.8) 371 (18.5%) 5.0*** (4.2; 5.9) 849 (42.3%) 6.7*** (5.8; 7.7) Bipolar 1 565 752 37 (6.5%) 12.7*** (8.0; 20.1) 235 (31.5%) 9.8*** (7.6; 12.5) 491 (65.5%) 17.0*** (14.0; 20.7) Any Anxiety Disorder 4700 5989 155 (3.3%) 5.5*** (4.3; 7.0) 1108 (16.9%) 4.3*** (3.8; 4.9) 2313 (38.7%) 5.6*** (5.1; 6.2) Specific Phobia 2035 2279 51 (2.5%) 4.3*** (3.0; 6.2) 353 (15.5%) 3.9*** (3.2; 4.6) 853 (37.5%) 5.4*** (4.7; 6.2) Social Anxiety 980 1255 37 (3.8%) 5.9*** (3.9; 8.9) 274 (22.0%) 5.9*** (4.9; 7.2) 552 (44.1%) 7.0*** (6.0; 8.3) Panic 1103 1811 52 (4.7%) 7.0*** (4.7; 10.3) 353 (19.6%) 5.2*** (4.2; 6.3) 797 (44.1%) 6.9*** (6.1; 7.9) Agoraphobia 549 690 22 (4.0%) 6.1*** (3.5; 10.6) 158 (23.1%) 6.3*** (4.8; 8.3) 342 (49.7%) 8.4*** (7.0; 10.1) Generalized Anxiety 1908 2708 82 (4.3%) 7.2*** (5.2; 10.0) 505 (18.7%) 4.8*** (4.1; 5.6) 1170 (43.2%) 6.7*** (6.0; 7.6) Posttraumatic Stress 1778 2337 87 (4.9%) 7.7*** (5.6; 10.6) 531 (22.8%) 6.3*** (5.4; 7.4) 1248 (53.4%) 9.8*** (8.7; 11.1) Eating Disordera 385 615 6 (1.6%) 2.3 (1.0; 5.6) 89 (14.5%) 3.8*** (2.7; 5.2) 251 (40.8%) 6.2*** (5.1; 7.5) Any Substance Use Disorder 5808 10929 352 (6.1%) 10.5*** (8.6; 12.7) 2671 (24.6%) 6.7*** (6.0; 7.6) 4449 (40.8%) 6.1*** (5.5; 6.7) Alcohol Abuse 5133 10000 306 (6.0%) 10.2*** (8.3; 12.5) 2388 (24.0%) 6.6*** (5.8; 7.4) 4032 (40.4%) 6.0*** (5.4; 6.6) Drug Abuse 1487 3548 154 (10.4%) 19.9*** (15.5; 25.5) 1342 (38.1%) 13.1*** (11.3; 15.1) 1960 (55.4%) 11.2*** (9.9; 12.5) Any personality disorderb NA 5745 315 (5.5%) 10.6*** (8.3; 13.5) 1650 (28.8%) 9.1*** (8.0; 10.4) 3754 (65.3%) 17.3*** (15.6; 19.1) Borderlineb NA 4300 250 (5.8%) 11.3*** (8.9; 14.3) 1240 (28.9%) 9.4*** (8.2; 10.7) 2911 (67.7%) 19.4*** (17.4; 21.7) Antisocialb NA 1600 132 (8.3%) 16.6*** (12.2; 22.6) 714 (44.8%) 18.5*** (15.7; 21.8) 1356 (84.8%) 49.8*** (41.8; 59.4) Schizotypalb NA 2438 148 (6.1%) 12.7*** (9.7; 16.6) 685 (28.2%) 8.8*** (7.4; 10.5) 1522 (62.4%) 15.0*** (13.1; 17.1) Sample is limited to people with data on functional impairment. Lower scores indicate poorer perceived functioning. *p<.05, **p<.01, ***p<.001. a Includes bulimia, anorexia nervosa. b Only lifetime personality disorder diagnoses are available. c Data weighted to adjust for non-response.

Prevention of violent behaviors (VB) in the early phase of psychosis (EPP) is a real challenge. Impulsivity was shown to be strongly related to VB, and different evolutions of impulsivity were noticed along treatments. One possible variable involved in the relationship between VB and the evolution of impulsivity is cannabis use (CU). The high prevalence of CU in EPP and its relationship with VB led us to investigate: 1/the impact of CU and 2/the impact of early CU on the evolution of impulsivity levels during a 3-year program, in violent and non-violent EPP patients. 178 non-violent and 62 violent patients (VPs) were followed-up over a 3 year period. Age of onset of CU was assessed at program entry and impulsivity was assessed seven times during the program. The evolution of impulsivity level during the program, as a function of the violent and non-violent groups of patients and CU precocity were analyzed with linear mixed-effects models. Over the treatment period, impulsivity level did not evolve as a function of the interaction between group and CU (coef. = 0.02, = 0.425). However, when including precocity of CU, impulsivity was shown to increase significantly only in VPs who start consuming before 15 years of age (coef. = 0.06, = 0.008). The precocity of CU in VPs seems to be a key variable of the negative evolution of impulsivity during follow-up and should be closely monitored in EPP patients entering care since they have a higher risk of showing VB.

Background Evidence has suggested an association between working memory impairment and violence in people with schizophrenia. However, the neural characteristics of working memory deficit in this patient population remain unclear. This study aimed to examine the neurobiological differences in brain activity using the working memory task in schizophrenia patients with and without a history of violent behavior. Method A total of 194 patients, including 106 with a history of severe violent behavior and 88 without such a history, as well as 66 healthy individuals, were recruited. All the participants were required to complete the n-back task (0-, 1-, and 2-back) under functional near-infrared spectroscopy. Results Patients with a history of violent behavior, but not those without, exhibited hyperactivity in the left dorsolateral prefrontal gyrus (all p  < 0.05) and hypoactivity in the triangular part of the inferior frontal gyrus (all p  < 0.05), as compared to healthy individuals. Compared to patient without a violent history, they demonstrated higher accuracy in 0- and 1-back tasks (both p  < 0.001), however, their response times were also significantly prolonged ( p  < 0.001; p  = 0.591). Conclusion This study revealed severe damage to the overall working memory in schizophrenia patients with a history of violent behavior, with abnormal activation of brain regions related to working memory and semantic processing, accompanied by compensatory brain responses.

This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study’s empirical focus is on the relocation of the Beijing municipal government, using time series data for Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. (2) Negative comments are more effective predictors than negative feelings; (3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; (4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is recommended that relevant departments establish a real-time monitoring system to track negative public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate communication, and promote transparency and educational campaigns to address the challenges of urban relocation. In future studies, methods such as expanding the sample size and adding indicators will be used to address the limitations of potential bias in sample data.

The objective of this study is to examine the prevalence of violence against teachers by students. The study included 175 teachers, five primary and five secondary schools. The age of respondents (teachers) ranges from 20 to 65, with average age being 44,33 years. The used instrument has assessed violence against teachers and has consisted of data about the characteristics of respondents, frequency and type of violence experienced from students.The results suggest that violence against teachers in primary and secondary schools in Zagreb taken in to sample is very much present. Since 74,3% teachers has experienced violence from their students during the year that kind of behavior is more of a rule than an exception. Students in primary and secondary schools show violent behavior against their teachers at an equal level. Male teachers, as opposed to female teachers, are more frequently victims of violent behavior (posting inappropriate content online) from their students. Also, there is a statistically significant correlation (negative) between age (years of service in school) and frequency of experienced violence from students.

Although violence and homicide are more prevalent in Colombia, South America than the US, the role of psychosocial factors in the violent behavior of Colombian adolescents remains unclear. The aim of this longitudinal study was to examine the interrelation of domains of personality, familial, peer, and ecological variables associated with violence in a community sample of adolescents from various self‐reported ethnic groups in Colombia. The sample consisted of 1,151 male adolescents selected from three Colombian cities. The participants were surveyed using structured interviews at two points in time over a 2‐year interval. Data were collected concerning adolescent personal attributes, family characteristics, peer, and ecological factors, including drug availability and the prevalence of violence in the community. The dependent variable was the self‐reported frequency of the adolescent's violent behavior. The results supported a model in which violent behavior was correlated independently over time with a number of risk factors from several domains. Evidence for the hypothesized mediated effects of the familial monitoring and bonding domain, the peer domain, the ecological domain, and prior victimization related to personal attributes and contemporaneous violence and the adolescent's violent behavior 2 years later was also found. The findings suggest the use of specific intervention procedures with adolescents to prevent their subsequent violent behavior.