Resilience and Recovery Style: a Retrospective Study on Associations Among Personal Resources, Symptoms, Neurocognition, Quality of Life and Psychosocial Functioning in Psychotic Patients

Personal resources have been identified as important factors in predicting patient healing or symptoms control in schizophrenia. This observational retrospective study aims to explore the influence of resilience and recovery style on the modalities of clinical presentation of the disease, as well as individual functioning and quality of life. Participants were patients affected by schizophrenia spectrum disorders assessed at different mental health facilities. The rating scales considered are the following: Resilience Scale 10-items (RS); Recovery Style Questionnaire (RSQ); Montreal Cognitive Assessment (MoCA); Schizophrenia Quality of Life Scale (SQLS); Life Skills Profile (LSP); Positive and Negative Syndrome Scale (PANSS). Forty-four patients fulfilled the inclusion criteria. The mean age was 46 years; the average length of the history of the disease at recruitment was 23 years with an average age at first episode of psychosis (FEP) of 23 years. General psychopathology, neurocognition, and integration recovery style can predict psychosocial functioning and explain ~54% of the LSP variance; RS total score and PANSS general psychopathology score can predict and explain ~29% of the LSP variance. A negative association between PANSS general psychopathology and LSP total score supports the need to reduce first the symptomatology, and then successfully apply other types of interventions. A strong positive association between neurocognition and life functioning was detected, showing that deficits in neurocognition have proved to be important predictors of the functional outcome. Integration was also proven to be significantly associated with a good functional outcome. Psychotic symptoms turn out to be a negative predictive factor, whereas resilience can be hypothesized as a protective factor. Resilience and recovery style "integration" can be considered as two complementary predictive resources for a good outcome; this result supports the need to set up personalized treatments, based on the characteristics of the patients.