No abstract

Empirical literature has yielded a positive association between psychopathy levels and suicide attempts. This association is centred around impulsivity and disinhibitory facets of psychopathy, whereas suicide and emotional poverty remain independent. Evidence about the relation between suicide and psychopathy in mentally disordered offenders is not conclusive. The present work explores the relation between several measures of antisocial personality, suicide attempt and deliberate self mutilation in a sample of inmates from a forensic psychiatric hospital. Results support the association between disinhibitory aspects of personality and suicide in this population.

Background Italy was one of the first European countries to be affected by Covid-19. Due to the severity of the pandemic, the Italian government imposed a nationwide lockdown which had a great impact on the population, especially adolescents. Distance-learning, moving restrictions and pandemic-related concerns, resulted in a particularly stressful situation. Objective This cross-sectional study aims to analyse substance consumption and its associated factors during the Covid-19 lockdown imposed by the Italian government. Methods ESPAD is a questionnaire that is administered yearly in Italian high schools. In 2020, it was administered online during dedicated hours of distance learning, collecting data from 6027 Italian students (52.4% were male) aged 15–19. Data collected from the 2020 questionnaire was matched with that collected in 2019, in order to make them comparable. Results The prevalence of consumption of each substance decreased during the restriction period, and the most used substance during the lockdown period was alcohol (43.1%). There were some changes in factors associated with psychoactive substance use, especially painkillers and non-prescription drugs. For instance, unlike what was observed in the 2019 model, in 2020 spending money without parental control was associated with painkillers and non-prescription drug use while risk perception was not. Conclusions The restrictions and the increased difficulties in obtaining psychoactive substances did not prevent their consumption, and students with particular risk factors continued to use them, possibly changing the substance type of substance. This information is useful in order to better understand adolescents’ substance use during the ongoing pandemic.

Purpose This article reports on an ongoing research project, which is aimed at implementing advanced probabilistic models for real-time identification of hazardous events at construction sites. The model has intelligent capabilities for near real-time automated recognition of hazardous events during the execution phase. To achieve this, features of Bayesian Networks have been exploited. In addition, inputs to the model are assumed to be provided by a pervasive monitoring system deployed on the site. The need for this kind of intelligent tool is determined by the complexity inherent in construction sites, due to a variety of reasons, such as heterogeneity of the actors, the simultaneous nature of operations, harsh contextual conditions, and the only partially efficient current approach based on health and safety plans. Hence, this model is proposed as a support tool for health and safety coordinators for supervision of sites as they cannot guarantee a continuous physical presence. Method Given that there are no long-time series on past occurrences of hazardous events in all the potential contextual combinations presently available, the probabilistic models cannot be learned just through datasets. For that reason, the available data have been integrated with expert opinions. In particular, the conditional probabilities of the Bayesian networks are estimated by an elicitation process of subjective knowledge from the opinions of experts. The complexity of the phenomena under analysis are modelled as a tree structure with several levels (corresponding to the work-breakdown structure hierarchy), which itself is based on the top-down technique; it provides therefore a clear view of the global picture. The built-hierarchical tree allows the expert to weigh more easily causal relationships involved and also to define the qualitative structure of the net. Furthermore, the article describes and tests how conditional probabilities of the variables in the networks can be estimated, through gathering and interviewing groups of stakeholders and experts. Results & Discussion Our research has led to the definition of a probabilistic model using elicitation techniques for subjective knowledge. Furthermore, the development of such a model is part of a wider system relying on the implementation of a real-time monitoring network.

Purpose This article reports on an ongoing research project, which is aimed at implementing advanced probabilistic models for real-time identification of hazardous events at construction sites. The model has intelligent capabilities for near real-time automated recognition of hazardous events during the execution phase. To achieve this, features of Bayesian Networks have been exploited. In addition, inputs to the model are assumed to be provided by a pervasive monitoring system deployed on the site. The need for this kind of intelligent tool is determined by the complexity inherent in construction sites, due to a variety of reasons, such as heterogeneity of the actors, the simultaneous nature of operations, harsh contextual conditions, and the only partially efficient current approach based on health and safety plans. Hence, this model is proposed as a support tool for health and safety coordinators for supervision of sites as they cannot guarantee a continuous physical presence. Method Given that there are no long-time series on past occurrences of hazardous events in all the potential contextual combinations presently available, the probabilistic models cannot be learned just through datasets. For that reason, the available data have been integrated with expert opinions. In particular, the conditional probabilities of the Bayesian networks are estimated by an elicitation process of subjective knowledge from the opinions of experts. The complexity of the phenomena under analysis are modelled as a tree structure with several levels (corresponding to the work-breakdown structure hierarchy), which itself is based on the top-down technique; it provides therefore a clear view of the global picture. The built-hierarchical tree allows the expert to weigh more easily causal relationships involved and also to define the qualitative structure of the net. Furthermore, the article describes and tests how conditional probabilities of the variables in the networks can be estimated, through gathering and interviewing groups of stakeholders and experts. Results & Discussion Our research has led to the definition of a probabilistic model using elicitation techniques for subjective knowledge. Furthermore, the development of such a model is part of a wider system relying on the implementation of a real-time monitoring network.

No abstract

Cyberbullying and psychoactive substance use are two common risky behaviors among adolescents, and a growing body of documents observe associations between these two phenomena. The present systematic review aims to clarify this association, analyzing the use of both legal and illegal psychoactive substances and all cyberbullying roles. To this purpose, a systematic search on PubMed, Scopus and PsycInfo databases was conducted, focusing on adolescents aged between 10 and 20 years old. The review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and it includes a total of fifty original articles. The majority of them observed a positive association between cyberbullying involvement and the use of psychoactive substances, especially tobacco and alcohol. Regarding moderator factors, some studies observed the aforementioned association only among girls. Moreover, controlling for gender, delinquent friends and low parental support, this association became not significant. Nevertheless, there was a lack of information about the role of those who witnessed cyberbullying, and the included articles showed mixed results regarding illegal substance use. The findings highlighted the need for further research in order to better clarify the association between cyberbullying and substance use, and equally explore all cyberbullying roles and substance types.

This article reports on the current state of an ongoing research project which is aimed at implementing intelligent models for hardly predictable hazard scenarios identification in construction sites.As past evidences showed that no programmatic action can deal with the unpredictable nature of many risk dynamics, we tried to survey on how the current approach for safety management in the construction industry could be improved. In our previous research the use of Bayesian networks elicited from subjective knowledge were preliminarily tested. Those networks might be meant as a reliable knowledge map about accident dynamics and they showed that a relevant ratio of occurrences fall in "hardly predictable hazards" class, which cannot be warded off by programmatic safety measures.This paper reports the second outcome of our research project, which focused on the development of first elementary fragments, regarding the occurrence of a possible "hardly predictable scenario". Instead of experts' contributions (who, over their carrier, seldom incurred in accidents), we used "legal cases" as an accurate source of information. They suggested which categories of "hidden hazard scenarios" are more likely to happen. We found that the most frequent hidden hazard scenarios are linked to operator's negligence and abnormal behavior, e.g. irregular removal of scaffolding's components, unprotected openings, improper use of PPE, etc. Every pattern determined by legal cases has been formalized by a fragment (i.e. elementary network) of the overall Bayesian network.Finally, all the elementary networks were integrated into a comprehensive intelligent tool for realtime hardly predictable hazards prevention. The final setup, asked for interfacing these intelligent models to a low-level sensor network and used to feed them with inputs about the current state of the context, is discussed too.