A Systematic Review and Meta-analysis: Paternal Anxiety and the Emotional and Behavioral Outcomes in Their Offspring.

OBJECTIVE: Anxiety disorders are highly prevalent worldwide; however, the literature lacks a meta-analytic quantification of the risk posed by fathers' anxiety for offspring development. This systematic review and meta-analysis aimed to provide a comprehensive estimate of the magnitude of the association between paternal anxiety and emotional and behavioral problems of offspring. METHOD: In February 2022, Web of Science, Ovid (Embase, MEDLINE, PsycINFO), Trip Database, and ProQuest were searched to identify all quantitative studies that measured anxiety in fathers and emotional and/or behavioral outcomes in offspring. No limits were set for offspring age, publication language, or publication year. Summary estimates were extracted from the primary studies. Meta-analytic random-effects 3-level models were used to calculate correlation coefficients. Quality was assessed using the Newcastle-Ottawa Scale. The study protocol was preregistered with PROSPERO (CRD42022311501) and adhered to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) reporting guidelines. RESULTS: Of 11,746 records identified, 98 were included in the meta-analysis. Small but significant associations were found between paternal anxiety and offspring emotional and behavioral problems overall (r = 0.16, 95% CI [0.13, 0.19]) and behavioral (r = 0.19, 95% CI [0.13, 0.24]), emotional (r = 0.15, 95% CI [0.12, 0.18]), anxiety (r = 0.13, 95% CI [0.11, 0.16]), and depression (r = 0.13, 95% CI [0.03, 0.23]) problems. Some significant moderators were identified. CONCLUSION: Paternal mental health is associated with offspring development, and the offspring of fathers with anxiety symptoms or disorders are at increased risk of negative emotional and behavioral outcomes, in line with the principles of multifinality and pleiotropy. The substantial heterogeneity among studies and the overrepresentation of White European American groups in this literature highlight the need for further research. DIVERSITY & INCLUSION STATEMENT: While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list.

The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies.

OBJECTIVE: The present review is intended to provide an up-to-date overview of the strategies available to detect malingered symptoms following whiplash. Whiplash-associated disorders (WADs) represent the most common traffic injuries, having a major impact on economic and healthcare systems worldwide. Heterogeneous symptoms that may arise following whiplash injuries are difficult to objectify and are normally determined based on self-reported complaints. These elements, together with the litigation context, make fraudulent claims particularly likely. Crucially, at present, there is no clear evidence of the instruments available to detect malingered WADs. METHODS: We conducted a targeted literature review of the methodologies adopted to detect malingered WADs. Relevant studies were identified via Medline (PubMed) and Scopus databases published up to September 2020. RESULTS: Twenty-two methodologies are included in the review, grouped into biomechanical techniques, clinical tools applied to forensic settings, and cognitive-based lie detection techniques. Strengths and weaknesses of each methodology are presented, and future directions are discussed. CONCLUSIONS: Despite the variety of techniques that have been developed to identify malingering in forensic contexts, the present work highlights the current lack of rigorous methodologies for the assessment of WADs that take into account both the heterogeneous nature of the syndrome and the possibility of malingering. We conclude that it is pivotal to promote awareness about the presence of malingering in whiplash cases and highlight the need for novel, high-quality research in this field, with the potential to contribute to the development of standardised procedures for the evaluation of WADs and the detection of malingering.

False Identity Detection Using Complex Sentences.

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models.