The uncontrolled spread of the coronavirus disease 2019 (COVID-19) has called for unprecedented measures, to the extent that the Italian government has imposed a quarantine on the entire country. Quarantine has a huge impact and can cause considerable psychological strain. The present study aims to establish the prevalence of psychiatric symptoms and identify risk and protective factors for psychological distress in the general population. An online survey was administered from 18–22 March 2020 to 2766 participants. Multivariate ordinal logistic regression models were constructed to examine the associations between sociodemographic variables; personality traits; depression, anxiety, and stress. Female gender, negative affect, and detachment were associated with higher levels of depression, anxiety, and stress. Having an acquaintance infected was associated with increased levels of both depression and stress, whereas a history of stressful situations and medical problems was associated with higher levels of depression and anxiety. Finally, those with a family member infected and young person who had to work outside their domicile presented higher levels of anxiety and stress, respectively. This epidemiological picture is an important benchmark for identifying persons at greater risk of suffering from psychological distress and the results are useful for tailoring psychological interventions targeting the post-traumatic nature of the distress.

The spread of coronavirus disease 2019 (COVID-19) has called for unprecedented measures, including a national lockdown in Italy. The present study aimed at identifying psychological changes (e.g., changes in depression, stress, and anxiety levels) among the Italian public during the lockdown period, in addition to factors associated with these changes. An online follow-up survey was administered to 439 participants (original sample = 2766), between 28 April and 3 May 2020. A paired sample t-test tested for differences in stress, anxiety, and depression over the period. Multivariate regression models examined associations between sociodemographic variables, personality traits, coping strategies, depression, and stress. Results showed an increase in stress and depression over the lockdown, but not anxiety. Negative affect and detachment were associated with higher levels of depression and stress. Higher levels of depression at the start of the lockdown, as well as fewer coping strategies and childlessness, were associated with increased depression at follow-up, whereas higher levels of stress at the start of the lockdown and younger age were associated with higher stress at follow-up. These results may help us to identify persons at greater risk of suffering from psychological distress as a result lockdown conditions, and inform psychological interventions targeting post-traumatic symptoms.

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance.

Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers’ performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T-scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55–65%), whereas temporal features resulted to be more useful (accuracies 70–75%). By contrast, temporal features and T-scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

Introduction: The present systematic review aims to synthesize and critically analyze the use of serious games in the professional training and education of psychologists and psychology students.Methods: Following PRISMA guidelines, database searches from inception to July 2023 (PsycINFO, PubMed, Web of Science, and Scopus) yielded 4,409 records, of which 14 met the eligibility criteria, including 17 studies. Quality assessment was performed using the Newcastle-Ottawa Scale and the Risk of Bias Tool for Randomized Trials.Results: The review identified three pivotal areas where serious games demonstrated significant educational impact: enhancing psychological traits and attitudes (e.g., prejudice, empathy), promoting theoretical knowledge acquisition (e.g., biopsychology), and developing professional skills (e.g., investigative interview with children). Serious games, particularly those providing feedback and modeling, significantly enhance the quality of learning and training for psychology students and professionals.Discussion: Key findings revealed that serious games operate by offering realistic, engaging, and flexible learning environments while mitigating risks associated with real-world practice. Methodological limitations, including moderate to high risk of bias in many studies, especially those that relied on cross-sectional data, underscore the need for rigorous designs and long-term evaluations. Practical implications suggest integrating serious games into curricula to address gaps in experiential learning for psychologists, facilitating skill development and knowledge retention. Future research should explore the long-term impact of serious games on professional competencies and assess their applicability across diverse educational contexts.

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering—accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)—each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants. We analyzed the Structured Inventory of Malingered Symptomatology scales and the Minnesota Multiphasic Personality Inventory-2 Restructured Form validity scales of 132 individuals with a diagnosed adjustment disorder with mixed anxiety and depressed mood who had undergone assessment for psychiatric/psychological damage. The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator.