The Relationship between Dark Triad Personality Traits and Sexting Behaviors among Adolescents and Young Adults across 11 Countries.

BACKGROUND: Sexting is an increasingly common phenomenon among adolescents and young adults. Some studies have investigated the role of personality traits in different sexting behaviors within mainstream personality taxonomies like Big Five and HEXACO. However, very few studies have investigated the role of maladaptive personality factors in sexting. Therefore, the present study investigated the relationship between Dark Triad Personality Traits and experimental (i.e., sharing own sexts), risky (i.e., sexting under substance use and with strangers), and aggravated sexting (i.e., non-consensual sexting and sexting under pressure) across 11 countries. METHODS: An online survey was completed by 6093 participants (Mage = 20.35; SDage = 3.63) from 11 different countries which covered four continents (Europe, Asia, Africa, and America). Participants completed the Sexting Behaviors Questionnaire and the 12-item Dark Triad Dirty Dozen scale. RESULTS: Hierarchical regression analyses showed that sharing own sexts was positively predicted by Machiavellianism and Narcissism. Both risky and aggravated sexting were positively predicted by Machiavellianism and Psychopathy. CONCLUSIONS: The present study provided empirical evidence that different sexting behaviors were predicted by Dark Triad Personality Traits, showing a relevant role of Machiavellianism in all kinds of investigated sexting behaviors. Research, clinical, and education implications for prevention programs are discussed.

Relationships Between Medical Doctors' Personality Traits and Their Professional Risk Perception.

Medical decision-making is often related to risk and uncertainty, but existing research does not offer a comprehensive approach to this matter. We discuss the necessity to study cognitive representations of risks (CRRs), which we define as the subject's images of risky situations, possible outcomes, and alternative decisions. The psychometric approach towards risk assessment often involves the evaluation of different risks, but we aim to create such a list from medical professionals' expert knowledge. Via qualitative analysis, CRRs were obtained from interviews with practicing doctors from Russia (N = 24). The list includes 21 risks from real-life medical practice, with seven aspects for numerical evaluation each. Then, practicing doctors (N = 64) evaluated CRRs along with filling risk-related personality traits questionnaires: Personal Decision-Making Factors Questionnaire, Melbourne Decision Making Questionnaire, Ten Item Personality Measure, and Budner's Intolerance of Ambiguity Scale. A correlational analysis showed interconnections between most CRRs aspects, with predictability and negative outcome probability seemingly being the central aspects of the risk assessment. CRRs aspects were also found to be gender- and experience-specific, with female doctors and younger specialists being more sensitive to professional risks. Personality traits in relation to CRRs aspects, medical experience and gender are also discussed.

Computer-Based Prognostic Task Measurements as Indicators of Uncertainty Acceptance.

There were few studies of individual differences in prognostic decision-making from the psychological point of view; most of them focused on the differences between novices and experts making the prognoses. In this study, we suggested a new task that matched the criteria of a prognostic one, was computerized, and did not require expertise in any field of knowledge. Thus, the proposed method investigated how people processed information and controlled uncertainty in prognostic tasks. On a sample of 78 people aged 17-66, we used a quasi-experimental design to find the patterns of the proposed task parameters and how they correlated with personality and cognitive variables. Five well-known personality questionnaires accessing traits, known to be included in decision-making regulation, were used along with a cognitive abilities test to measure those variables. Two patterns were identified via cluster analysis. Differences in intolerance for uncertainty were demonstrated for the people from two identified clusters. Those patterns could be interpreted as uncertainty control strategies for decision-making grounding in prognostic tasks.