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Parental mediation in the use of Information and Communication Technologies (ICTs) is a protective factor against adolescents' online risk behaviours. This study aimed to design an assessment scale of parental mediation referred by minors and to explore its structure and psychometric properties. A total of 560 secondary education students (47.5% girls) informed, in addition to parental mediation, about their habits of connection and online communication, privacy and risky online behaviour, and cyber-victimization. Data revealed a Parental Mediation Scale for ICTs (EMP-TIC) composed of 28 items distributed in five dimensions: active regulation, restrictive regulation, co-use, software monitoring and personal monitoring, which explain 55.98% of the variance. The dimensions of monitoring and co-use showed significant positive correlations with cyber-victimization. Higher scores in parental mediation are associated with greater privacy. The Parental Mediation Scale (EMP-TIC) structure is coherent to previous theoretical proposals. EMP-TIC shows adequate psychometric properties and provides information about the parental patterns in the use of ICTs. The scale can be used as a total score of parental mediation or for each of the five included domains. [Spanish translation by Mercè Rius.]

This study is aimed at analysing the predictive power of different psychosocial and personality variables on the consumption or non-consumption of nicotine in a teenage population using different classifica-tion techniques from the field of Data Mining. More specifically, we ana-lyse ANNs - Multilayer Perception (MLP)' Radial Basis Functions (RBF) and Probabilistic Neural Networks (PNNs) -- decision trees, the logistic re-gression model and discriminant analysis. To this end, we worked with a sample of 2666 teenagers, 1378 of whom do not consume nicotine while 1288 are nicotine consumers. The models analysed were able to discrimi-nate correcdy between both types of subjects within a range of 77.39% to 78.20%, achieving 91.29% sensitivity and 74.32% specificity. With this study, we place at the disposal of specialists in addictive behaviours a set of advanced statistical techniques that are capable of simultaneously pro-cessing a large quantity of variables and subjects, as well as learning com-plex patterns and relationships automatically, in such a way that they are very appropriate for predicting and preventing addictive behaviour. [ABSTRACT FROM AUTHOR]

Media pressure from gambling advertising has reached worrying levels. It constitutes a risk to the mental health of young people and adolescents by promoting a favourable attitude towards gambling, a higher frequency of gambling and a perception of greater accessibility. Currently, there is no instrument available to assess the impact of gambling advertising., Objective: The present study aims to adapt and validate the Impact of Gambling Advertising Scale (IGAS)., Design: The IGAS scale was translated from English ensuring its linguistic, conceptual and metric equivalence. The psychometric properties were then tested. SITE: Comunidad Valenciana (España)., Participants: 1724 adolescents with a mean age of 16.52 years (SD=.759)., Intervention: Self-administered, paper-based questionnaire in a single measure., Main Measurements: Advertising impact, and gambling intention, severity and availability., Results: Internal consistency and two-half reliability were good, Cronbach's α=.782 and α=.70, respectively. Confirmatory factor analysis concluded that the Spanish version replicates the original three-dimensional version. Convergent validity analyses showed direct and significant relationships with different aspects of gambling behaviour, and other predictors., Conclusion: The adapted version of the IGAS is a reliable and valid measure for the assessment of the impact of advertising on adolescents. The scale is a useful instrument for the diagnosis of risk level and the evaluation of preventive interventions., (Copyright © 2021 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

Objectives: To review changes occurring over time in cases of medication overdose attended by an emergency department., Material and Methods: Retrospective review of epidemiologic and care variables related to drug poisonings in a university teaching hospital in 2007 and 2017. We used multivariate analysis to compare the 2 years., Results: A total of 750 cases were included; 438 (58.4%) were from 2007. Fewer cases were seen in 2017 even though the total numbers of emergencies and poisonings had risen (P<.001). Fewer cases were suicides or suicide attempts in 2017 (P<.001), and digestive tract decontamination and antidotes were used less often (P<.001 and P=.007, respectively). Admissions (P=.004) and voluntary self-discharges or patient losses were also down in 2017 (P=.03). However, multidrug poisonings increased (P=.001), especially in the context of recreational drug use by men. Benzodiazepine overdoses accounted for most of such cases (65.1%)., Conclusion: Medication overdoses seem to be decreasing, although the proportion of men overdosing is rising. Suicide attempts, the abuse of specific medications, and admissions also seem to be decreasing.

This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), whereby different modelling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030). Although the overall accuracy obtained (% correct predictions) is very similar in the three models analyzed, the Artificial Neural Network (ANN) technique generates the most accurate model (64.1%), followed by Decision Trees (DT) (62.3%) and Naïve Bayes (NB) (59.9%).