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Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).
SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.
The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).
We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
When facing a traumatic event, some people may experience positive changes, defined as posttraumatic growth (PTG).
Understanding the possible positive consequences of the pandemic on the individual level is crucial for the development of supportive psychosocial interventions. The present paper aims to: 1) evaluate the levels of PTG in the general population; 2) to identify predictors of each dimension of post-traumatic growth.
The majority of the sample (67%, N = 13,889) did not report any significant improvement in any domain of PTG. Participants reported the highest levels of growth in the dimension of “appreciation of life” (2.3 ± 1.4), while the lowest level was found in the “spiritual change” (1.2 ± 1.2). Female participants reported a slightly higher level of PTG in areas of personal strength (p < .002) and appreciation for life (p < .007) compared to male participants, while no significant association was found with age. At the multivariate regression models, weighted for the propensity score, only the initial week of lockdown (between 9-15 April) had a negative impact on the dimension of “relating to others” (B = −.107, 95% CI = −.181 to −.032, p < .005), while over time no other effects were found. The duration of exposure to lockdown measures did not influence the other dimensions of PTG.
The assessment of the levels of PTG is of great importance for the development of ad hoc supportive psychosocial interventions. From a public health perspective, the identification of protective factors is crucial for developing ad-hoc tailored interventions and for preventing the development of full-blown mental disorders in large scale.
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