The aim of this study was to evaluate the degree of insight and resistance in a sample of obsessive-compulsive patients, and the predictive value of poor insight with respect to response to treatment with serotonin reuptake inhibitors (SRIs). Ninety-three patients fulfilling DSM-IV criteria for obsessive-compulsive disorder were evaluated. Seventy patients were treated with an SRI in a 24-week open-label trial. Sixteen percent of the patients did not recognize obsessions and compulsions as unreasonable or senseless. Fifty-two percent of the patients did not try to resist, 72% had little or no control over obsessions, and 64% were not able to exercise an effective control over compulsions. Patients with poor insight had a greater severity of obsessive-compulsive symptoms, a higher rate of schizophrenia spectrum disorders in their first-degree relatives and a higher frequency of a history of psychiatric disorders during childhood. Fifty-two percent of the patients with normal insight responded to SRIs, whereas none of the patients with poor insight were found to be responders. These results suggest the utility of the assessment of insight and resistance in obsessive-compulsive patients, also for the prediction of response to treatment with SRIs.
During the last period of lockdown because of the Covid-19 pandemic influence, a lot of mental health professionals were able to continue working online, assisting their patients from home too. Even if there are still some doubts about videoconferencing psychotherapy (VCP), a lot of studies indicate that VCP can be feasible and clinically effective, in fact the online therapy is becoming increasingly popular, reducing distances even during the current pandemic. The aim of this study is to analyze the degree of satisfaction after using VCP in a sample of psychotherapists freely recruited through the publication on social media of a specially created questionnaire. The collected
Background:Although different personality traits have often been associated with different levels of mental activity and cognitive functioning, no previous studies have evaluated the association in a sample that mirrors a nationally-representative sample of elderly individuals.Objective:To evaluate the association between personality traits and neurocognitive functioning among individuals 51 years and older using the Cognition and Aging in the USA (CogUSA) database.Methods:We analyzed the association between personality traits and neurocognitive scores derived from Waves I and II of the study. Neurocognitive functions were modeled as an outcome variable using the Big Five Personality Traits as predictors.Results:All personality traits were associated with higher education except Conscientiousness. Older age was associated with higher levels of the Agreeableness and Openness traits. Extraversion, Conscientiousness and Openness were positively associated with increased neurocognitive function and self-rated present memory. Extraversion and Openness also had a positive association with long-term retrieval. Agreeableness was negatively associated with several neurocognitive functions, while Neuroticism was negatively associated with memory and cognitive effort.Conclusion:Extraversion, Conscientiousness and Openness personality traits are associated with good cognitive health. Individuals scoring high in Neuroticism and Agreeableness might benefit from tailored cognitive interventions to prevent age-related cognitive decline.
Starting with the provision of ready-to-use infrastructures, such as storage and compute resources, cloud computing quickly became a flexible, cost-effective and complete environment for a wide range of IT services offered over the Internet. A growing number of cloud providers started to expose their own services to the market, to answer consumer's need, engaging a competition in the attempt to offer the easiest access to resources and the wider catalogue of services. Being integrated with proprietary services and infrastructures, these offerings makes difficult to switch between different underlying technologies, so that the customer is tied to the service provider's strategy. This lack in standards interfaces, service requirements and technologies brings into the cloud the vendor lock-in problem. Due to this complex scenery, a categorization of services to support the choice of the right solution, as well as a semantic and computable description of services that enables the comparison and the mapping between different providers' services, proves to be extremely appealing. In an attempt to take a first important step in this context we propose the semantic description of some cloud services, exposing them in terms of functionalities, parameters exchanged, and collaboration between services. In particular in this paper we present the semantic representation of Microsoft Windows Azure APIs, describing functional and non-functional properties of the services.
Dolce et al. Machine Learning to Complement Explanatory Modeling prediction accuracy of 88.5%, that is 25.6% of incorrectly classified), compared to the predictions obtained using all the original items (260 items with a prediction accuracy of 74.4%). At the same time, it reduced the redundancy of the items and eliminated those with less consistency.
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