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Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning

Published online by Cambridge University Press:  11 October 2022

Linda A. Antonucci ,
Giulio Pergola ,
Antonio Rampino ,
Paola Rocca ,
Alessandro Rossi ,
Mario Amore ,
Eugenio Aguglia ,
Antonello Bellomo ,
Valeria Bianchini  and
Claudio Brasso
...Show all authors
Linda A. Antonucci
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Giulio Pergola
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Antonio Rampino
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Paola Rocca
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
Alessandro Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
Mario Amore
Affiliation:
Section of Psychiatry, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
Eugenio Aguglia
Affiliation:
Department of Clinical and Molecular Biomedicine, Psychiatry Unit, University of Catania, Catania, Italy
Antonello Bellomo
Affiliation:
Psychiatry Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
Valeria Bianchini
Affiliation:
Unit of Psychiatry, Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
Claudio Brasso
Affiliation:
Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
Paola Bucci
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Bernardo Carpiniello
Affiliation:
Section of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Cagliari, Italy
Liliana Dell'Osso
Affiliation:
Section of Psychiatry, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
Fabio di Fabio
Affiliation:
Department of Neurology and Psychiatry, “La Sapienza” University of Rome, Rome, Italy
Massimo di Giannantonio
Affiliation:
Department of Neuroscience and Imaging, “G. D'Annunzio” University, Chieti, Italy
Andrea Fagiolini
Affiliation:
Department of Molecular Medicine and Clinical Department of Mental Health, University of Siena, Siena, Italy
Giulia Maria Giordano
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Matteo Marcatilli
Affiliation:
Department of Psychiatry, University of Milan, Milan, Italy
Carlo Marchesi
Affiliation:
Department of Neuroscience, Psychiatry Unit, University of Parma, Parma, Italy
Paolo Meneguzzo
Affiliation:
Psychiatric Clinic, Department of Neurosciences, University of Padua, Padua, Italy
Palmiero Monteleone
Affiliation:
Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana” Section of Neuroscience, University of Salerno, Salerno, Italy
Maurizio Pompili
Affiliation:
Department of Neurosciences, Mental Health and Sensory Organs, S. Andrea Hospital, “La Sapienza” University of Rome, Rome, Italy
Rodolfo Rossi
Affiliation:
Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
Alberto Siracusano
Affiliation:
Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, “Tor Vergata” University of Rome, Rome, Italy
Antonio Vita
Affiliation:
Psychiatric Unit, School of Medicine, University of Brescia, Brescia, Italy Department of Mental Health, Spedali Civili Hospital, Brescia, Italy
Patrizia Zeppegno
Affiliation:
Department of Translational Medicine, Psychiatric Unit, University of Eastern Piedmont, Novara, Italy
Silvana Galderisi
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Alessandro Bertolino*
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
Mario Maj
Affiliation:
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy
Italian Network for Research on Psychoses
Affiliation:
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
*
Author for correspondence: Alessandro Bertolino, E-mail: alessandro.bertolino@uniba.it
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Abstract

Background

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).

Methods

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.

Results

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).

Conclusions

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.

Keywords

Italian network for research on psychosesmachine learningpersonalized interventionsresilienceschizophrenia
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 12 , September 2023 , pp. 5717 - 5728
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

The members of the Italian Network for Research on Psychoses involved in this study are listed in the Appendix.

References

Alparone, F. R., Pagliaro, S., & Rizzo, I. (2015). The words to tell their own pain: Linguistic markers of cognitive reappraisal in mediating benefits of expressive writing. Journal of Social and Clinical Psychology, 34(6), 495–507. https://doi.org/10.1521/jscp.2015.34.6.495.CrossRefGoogle Scholar
Antonucci, L. A., Fazio, L., Pergola, G., Blasi, G., Stolfa, G., Di Palo, P., … Italian Network for Research on, P. (2022a). Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophrenia Research, 240, 193–203. doi: 10.1016/j.schres.2021.12.029CrossRefGoogle ScholarPubMed
Antonucci, L. A., Penzel, N., Pergola, G., Kambeitz-Ilankovic, L., Dwyer, D., Kambeitz, J., … Koutsouleris, N. (2020a). Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology, 45(4), 613–621. doi: doi:10.1038/s41386-019-0532-3CrossRefGoogle ScholarPubMed
Antonucci, L. A., Penzel, N., Sanfelici, R., Pigoni, A., Kambeitz-Ilankovic, L., Dwyer, D., … Consortium, P. (2022b). Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression. The British Journal of Psychiatry, 1–17. doi: 10.1192/bjp.2022.16Google ScholarPubMed
Antonucci, L. A., Pergola, G., Pigoni, A., Dwyer, D., Kambeitz-Ilankovic, L., Penzel, N., … Bertolino, A. (2020b). A pattern of cognitive deficits stratified for genetic and environmental risk reliably classifies patients with schizophrenia from healthy control subjects. Biological Psychiatry, 87(8), 697–707. doi: 10.1016/j.biopsych.2019.11.007CrossRefGoogle ScholarPubMed
Antonucci, L. A., Raio, A., Pergola, G., Gelao, B., Papalino, M., Rampino, A., … Bertolino, A. (2021). Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition. BMC Psychology, 9(1), 47. doi: doi:10.1186/s40359-021-00552-3CrossRefGoogle ScholarPubMed
Antonucci, L. A., Taurino, A., Laera, D., Taurisano, P., Losole, J., Lutricuso, S., … Puntillo, F. (2020c). An ensemble of psychological and physical health indices discriminates between individuals with chronic pain and healthy controls with high reliability: A machine learning study. Pain and Therapy, 9(2), 601–614. doi: 10.1007/s40122-020-00191-3CrossRefGoogle ScholarPubMed
Arango, C., Dragioti, E., Solmi, M., Cortese, S., Domschke, K., Murray, R. M., … Fusar-Poli, P. (2021). Risk and protective factors for mental disorders beyond genetics: An evidence-based atlas. World Psychiatry, 20(3), 417–436. doi: 10.1002/wps.20894CrossRefGoogle ScholarPubMed
Bajaj, B., Gupta, R., & Pande, N. (2016). Self-esteem mediates the relationship between mindfulness and well-being. Personality and Individual Differences, 94, 96–100. https://doi.org/10.1016/j.paid.2016.01.020.CrossRefGoogle Scholar
Bemrose, H. V., Akande, I. O., & Cullen, A. E. (2021). Self-esteem in individuals at ultra-high risk for psychosis: A systematic review and meta-analysis. Early Interventions in Psychiatry, 15(4), 775–786. doi: 10.1111/eip.13034CrossRefGoogle ScholarPubMed
Benavides, C., Brucato, G., & Kimhy, D. (2018). Self-esteem and symptoms in individuals at clinical high risk for psychosis. The Journal of Nervous and Mental Disease, 206(6), 433–438. doi: 10.1097/NMD.0000000000000824CrossRefGoogle ScholarPubMed
Benjamini, Y., & Hockberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological, 57(1), 289–300.Google Scholar
Bonfiglio, N. S., Renati, R., Hjemdal, O., & Friborg, O. (2016). The resilience scale for adults in Italy: A validation study comparing clinical substance abusers with a nonclinical sample. Psychology of Addictive Behaviors, 30(4), 509–515. doi: 10.1037/adb0000176CrossRefGoogle ScholarPubMed
Bozikas, V., & Parlapani, E. (2016). Resilience in patients with psychotic disorder. Psychiatrike = Psychiatriki, 27(1), 13–16. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/27110878.Google ScholarPubMed
Carver, C. S., Scheier, M. F., & Weintraub, J. K. (1989). Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56(2), 267–283. doi: 10.1037//0022-3514.56.2.267CrossRefGoogle ScholarPubMed
Chen, H., Xu, J., Mao, Y., Sun, L., Sun, Y., & Zhou, Y. (2019). Positive coping and resilience as mediators between negative symptoms and disability Among patients with schizophrenia. Frontiers in Psychiatry, 10, 641. doi: 10.3389/fpsyt.2019.00641CrossRefGoogle ScholarPubMed
Chen, Y. L., & Kuo, P. H. (2020). Effects of perceived stress and resilience on suicidal behaviors in early adolescents. European Child and Adolescent Psychiatry, 29(6), 861–870. doi: 10.1007/s00787-019-01401-wCrossRefGoogle ScholarPubMed
Choi, J. W., Cha, B., Jang, J., Park, C. S., Kim, B. J., Lee, C. S., & Lee, S. J. (2015). Resilience and impulsivity in euthymic patients with bipolar disorder. Journal of Affective Disorders, 170, 172–177. doi: 10.1016/j.jad.2014.08.056CrossRefGoogle ScholarPubMed
Cohen, I. L., Schmidt-Lackner, S., Romanczyk, R., & Sudhalter, V. (2003). The PDD behavior inventory: A rating scale for assessing response to intervention in children with pervasive developmental disorder. Journal of Autism and Developmental Disorders, 33(1), 31–45. doi: 10.1023/a:1022226403878CrossRefGoogle ScholarPubMed
Crespo-Facorro, B., Such, P., Nylander, A. G., Madera, J., Resemann, H. K., Worthington, E., … Newton, R. (2021). The burden of disease in early schizophrenia - a systematic literature review. Current Medical Research and Opinion, 37(1), 109–121. doi: 10.1080/03007995.2020.1841618CrossRefGoogle ScholarPubMed
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118. doi: 10.1146/annurev-clinpsy-032816-045037CrossRefGoogle ScholarPubMed
Feder, A., Fred-Torres, S., Southwick, S. M., & Charney, D. S. (2019). The biology of human resilience: Opportunities for enhancing resilience across the life span. Biological Psychiatry, 86(6), 443–453. doi: 10.1016/j.biopsych.2019.07.012CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Correll, C. U., Arango, C., Berk, M., Patel, V., & Ioannidis, J. P. A. (2021). Preventive psychiatry: A blueprint for improving the mental health of young people. World Psychiatry, 20(2), 200–221. doi: 10.1002/wps.20869CrossRefGoogle ScholarPubMed
Galderisi, S., Mucci, A., Dollfus, S., Nordentoft, M., Falkai, P., Kaiser, S., … Gaebel, W. (2021). EPA Guidance on assessment of negative symptoms in schizophrenia. European Psychiatry, 64(1), e23. doi: 10.1192/j.eurpsy.2021.11CrossRefGoogle ScholarPubMed
Galderisi, S., Rossi, A., Rocca, P., Bertolino, A., Mucci, A., Bucci, P., … Italian Network For Research on, P. (2014). The influence of illness-related variables, personal resources and context-related factors on real-life functioning of people with schizophrenia. World Psychiatry, 13(3), 275–287. doi: 10.1002/wps.20167CrossRefGoogle ScholarPubMed
Galderisi, S., Rucci, P., Mucci, A., Rossi, A., Rocca, P., Bertolino, A., … Italian Network for Research on, P. (2020). The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: Stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients. World Psychiatry, 19(1), 81–91. doi: 10.1002/wps.20700CrossRefGoogle ScholarPubMed
Gerlach, J., Korsgaard, S., Clemmesen, P., Lauersen, A. M., Magelund, G., Noring, U., … Casey, D. E. (1993). The St. Hans rating scale for extrapyramidal syndromes: Reliability and validity. Acta Psychiatrica Scandinavica, 87(4), 244–252. doi: 10.1111/j.1600-0447.1993.tb03366.xCrossRefGoogle ScholarPubMed
Gruber, M., Rumpold, T., Schrank, B., Sibitz, I., Otzelberger, B., Jahn, R., … Unger, A. (2018). Recover recovery style from psychosis: A psychometric evaluation of the German version of the Recovery Style Questionnaire (RSQ). Epidemiology and Psychiatric Sciences, 29, e4. doi: 10.1017/S2045796018000471CrossRefGoogle ScholarPubMed
Haeffel, G. J., & Grigorenko, E. L. (2007). Cognitive vulnerability to depression: Exploring risk and resilience. Child and Adolescent Psychiatric Clinics of North America, 16(2), 435–448, x. doi: 10.1016/j.chc.2006.11.005CrossRefGoogle ScholarPubMed
Harvey, J., & Boynton, K. (2021). Self-disclosure and psychological resilience: The mediating roles of self-esteem and self-compassion. Interpersona, 15(1), 90–104. https://doi.org/10.5964/ijpr.4533.CrossRefGoogle Scholar
Haug, E., Melle, I., Andreassen, O. A., Raballo, A., Bratlien, U., Oie, M., … Moller, P. (2012). The association between anomalous self-experience and suicidality in first-episode schizophrenia seems mediated by depression. Comprehensive Psychiatry, 53(5), 456–460. doi: 10.1016/j.comppsych.2011.07.005CrossRefGoogle ScholarPubMed
Hjemdal, O., Vogel, P. A., Solem, S., Hagen, K., & Stiles, T. C. (2011). The relationship between resilience and levels of anxiety, depression, and obsessive-compulsive symptoms in adolescents. Clinical Psychology & Psychotherapy, 18(4), 314–321. doi: 10.1002/cpp.719CrossRefGoogle ScholarPubMed
Iimura, S., & Taku, K. (2018). Gender differences in relationship between resilience and big five personality traits in Japanese adolescents. Psychological Reports, 121(5), 920–931. doi: 10.1177/0033294117741654CrossRefGoogle ScholarPubMed
Jongeneel, A., Pot-Kolder, R., Counotte, J., van der Gaag, M., & Veling, W. (2018). Self-esteem moderates affective and psychotic responses to social stress in psychosis: A virtual reality study. Schizophrenia Research, 202, 80–85. doi: 10.1016/j.schres.2018.06.042CrossRefGoogle ScholarPubMed
Kalisch, R. B. D., Basten, U., Boks, M. P., Bonanno, G. A., Brummelman, E., Chmitorz, A., … Kleim, B. (2017). The resilience framework as a strategy to combat stress-related disorders. Nature Human Behaviour, 1, 784–790. doi: 10.1038/s41562-017-0200-8CrossRefGoogle ScholarPubMed
Karatzias, T., Gumley, A., Power, K., & O'Grady, M. (2007). Illness appraisals and self-esteem as correlates of anxiety and affective comorbid disorders in schizophrenia. Comprehensive Psychiatry, 48(4), 371–375. doi: 10.1016/j.comppsych.2007.02.005CrossRefGoogle ScholarPubMed
Keefe, R. S., Fox, K. H., Harvey, P. D., Cucchiaro, J., Siu, C., & Loebel, A. (2011). Characteristics of the MATRICS consensus cognitive battery in a 29-site antipsychotic schizophrenia clinical trial. Schizophrenia Research, 125(2-3), 161–168. doi: 10.1016/j.schres.2010.09.015CrossRefGoogle Scholar
Kim, K. R., Song, Y. Y., Park, J. Y., Lee, E. H., Lee, M., Lee, S. Y., … Kwon, J. S. (2013). The relationship between psychosocial functioning and resilience and negative symptoms in individuals at ultra-high risk for psychosis. Australian and New Zealand Journal of Psychiatry, 47(8), 762–771. doi: 10.1177/0004867413488218CrossRefGoogle ScholarPubMed
Kobau, R., Seligman, M. E., Peterson, C., Diener, E., Zack, M. M., Chapman, D., & Thompson, W. (2011). Mental health promotion in public health: Perspectives and strategies from positive psychology. American Journal of Public Health, 101(8), e1–e9. doi: 10.2105/AJPH.2010.300083CrossRefGoogle ScholarPubMed
Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., … Consortium, P. (2021a). Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry, 78(2), 195–209. doi: 10.1001/jamapsychiatry.2020.3604CrossRefGoogle ScholarPubMed
Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., … Consortium, P. (2018). Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: A multimodal, multisite machine learning analysis. JAMA Psychiatry, 75(11), 1156–1172. doi: 10.1001/jamapsychiatry.2018.2165CrossRefGoogle ScholarPubMed
Koutsouleris, N., Worthington, M., Dwyer, D. B., Kambeitz-Ilankovic, L., Sanfelici, R., Fusar-Poli, P., … Cannon, T. D. (2021b). Toward generalizable and transdiagnostic tools for psychosis prediction: An independent validation and improvement of the NAPLS-2 risk calculator in the multisite PRONIA cohort. Biological Psychiatry, 90(9), 632–642. doi: 10.1016/j.biopsych.2021.06.023CrossRefGoogle ScholarPubMed
Lecomte, T., Corbiere, M., & Laisne, F. (2006). Investigating self-esteem in individuals with schizophrenia: Relevance of the self-esteem rating scale-short form. Psychiatry Research, 143(1), 99–108. doi: 10.1016/j.psychres.2005.08.019CrossRefGoogle ScholarPubMed
Magai, C., Consedine, N. S., Fiori, K. L., & King, A. R. (2009). Sharing the good, sharing the bad: The benefits of emotional self-disclosure among middle-aged and older adults. Journal of Aging and Health, 21(2), 286–313. doi: 10.1177/0898264308328980CrossRefGoogle ScholarPubMed
Maj, M., van Os, J., De Hert, M., Gaebel, W., Galderisi, S., Green, M. F., … Ventura, J. (2021). The clinical characterization of the patient with primary psychosis aimed at personalization of management. World Psychiatry, 20(1), 4–33. doi: 10.1002/wps.20809CrossRefGoogle ScholarPubMed
Mao, W., & Agyapong, V. I. O. (2021). The role of social determinants in mental health and resilience after disasters: Implications for public health policy and practice. Frontiers of Public Health, 9, 658528. doi: 10.3389/fpubh.2021.658528CrossRefGoogle ScholarPubMed
Mausbach, B. T., Bowie, C. R., Harvey, P. D., Twamley, E. W., Goldman, S. R., Jeste, D. V., & Patterson, T. L. (2008). Usefulness of the UCSD performance-based skills assessment (UPSA) for predicting residential independence in patients with chronic schizophrenia. Journal of Psychiatric Research, 42(4), 320–327. doi: 10.1016/j.jpsychires.2006.12.008CrossRefGoogle ScholarPubMed
Mausbach, B. T., Depp, C. A., Bowie, C. R., Harvey, P. D., McGrath, J. A., Thronquist, M. H., … Patterson, T. L. (2011). Sensitivity and specificity of the UCSD Performance-based Skills Assessment (UPSA-B) for identifying functional milestones in schizophrenia. Schizophrenia Research, 132(2-3), 165–170. doi: 10.1016/j.schres.2011.07.022CrossRefGoogle ScholarPubMed
Mausbach, B. T., Harvey, P. D., Pulver, A. E., Depp, C. A., Wolyniec, P. S., Thornquist, M. H., … Patterson, T. L. (2010). Relationship of the brief UCSD Performance-based Skills Assessment (UPSA-B) to multiple indicators of functioning in people with schizophrenia and bipolar disorder. Bipolar Disorders, 12(1), 45–55. doi: 10.1111/j.1399-5618.2009.00787.xCrossRefGoogle ScholarPubMed
Mayoral-van Son, J., Juncal-Ruiz, M., Ortiz-Garcia de la Foz, V., Cantarero-Prieto, D., Blazquez-Fernandez, C., Paz-Zulueta, M., … Crespo-Facorro, B. (2019). Understanding the direct and indirect costs of a first episode of psychosis program: Insights from PAFIP of Cantabria, Spain, during the first year of intervention. Early Interventions in Psychiatry, 13(5), 1182–1190. doi: 10.1111/eip.12752CrossRefGoogle ScholarPubMed
Millier, A., Schmidt, U., Angermeyer, M. C., Chauhan, D., Murthy, V., Toumi, M., & Cadi-Soussi, N. (2014). Humanistic burden in schizophrenia: A literature review. Journal of Psychiatric Research, 54, 85–93. doi: 10.1016/j.jpsychires.2014.03.021CrossRefGoogle ScholarPubMed
Morote, R., Hjemdal, O., Martinez Uribe, P., & Corveleyn, J. (2017). Psychometric properties of the Resilience Scale for Adults (RSA) and its relationship with life-stress, anxiety and depression in a Hispanic Latin-American community sample. PLoS One, 12(11), e0187954. doi: 10.1371/journal.pone.0187954CrossRefGoogle Scholar
Mucci, A., Rucci, P., Rocca, P., Bucci, P., Gibertoni, D., Merlotti, E., & Italian Network for Research on, P. (2014). The specific level of functioning scale: Construct validity, internal consistency and factor structure in a large Italian sample of people with schizophrenia living in the community. Schizophrenia Research, 159(1), 144–150. doi: 10.1016/j.schres.2014.07.044CrossRefGoogle Scholar
Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. doi: 10.1038/nbt1206-1565CrossRefGoogle ScholarPubMed
Pennebaker, J. W., Mayne, T. J., & Francis, M. E. (1997). Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology, 72(4), 863–871. doi: 10.1037//0022-3514.72.4.863CrossRefGoogle ScholarPubMed
Peralta, V., & Cuesta, M. J. (1994). Psychometric properties of the positive and negative syndrome scale (PANSS) in schizophrenia. Psychiatry Research, 53(1), 31–40. doi: 10.1016/0165-1781(94)90093-0CrossRefGoogle ScholarPubMed
Ribe, J. M., Salamero, M., Perez-Testor, C., Valero, A., & Garcia, M. C. (2015). Spanish version of the social network questionnaire in schizophrenia patients' caregivers. Schizophrenia Research, 164(1–3), 273–274. doi: 10.1016/j.schres.2015.03.008CrossRefGoogle ScholarPubMed
Ritsher, J. B., Otilingam, P. G., & Grajales, M. (2003). Internalized stigma of mental illness: Psychometric properties of a new measure. Psychiatry Research, 121(1), 31–49. doi: 10.1016/j.psychres.2003.08.008CrossRefGoogle ScholarPubMed
Romm, K. L., Rossberg, J. I., Hansen, C. F., Haug, E., Andreassen, O. A., & Melle, I. (2011). Self-esteem is associated with premorbid adjustment and positive psychotic symptoms in early psychosis. BMC Psychiatry, 11, 136. doi: 10.1186/1471-244X-11-136CrossRefGoogle ScholarPubMed
Ruschhaupt, M., Huber, W., Poustka, A., & Mansmann, U. (2004). A compendium to ensure computational reproducibility in high-dimensional classification tasks. Statistical Applications in Genetics and Molelcular Biology, 3, Article37, Epub 2004 Dec 19, 1-24. doi:10.2202/1544-6115.1078.Google ScholarPubMed
Rutten, B. P., Hammels, C., Geschwind, N., Menne-Lothmann, C., Pishva, E., Schruers, K., … Wichers, M. (2013). Resilience in mental health: Linking psychological and neurobiological perspectives. Acta Psychiatrica Scandinavica, 128(1), 3–20. doi: 10.1111/acps.12095CrossRefGoogle ScholarPubMed
Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020). Individualized diagnostic and prognostic models for patients with psychosis risk syndromes: A meta-analytic view on the state of the art. Biological Psychiatry, 88(4), 349–360. doi: 10.1016/j.biopsych.2020.02.009CrossRefGoogle ScholarPubMed
Satici, S. A., Kayis, A. R., & Akin, A. (2013). Predictive role of authenticity on psychological vulnerability in Turkish university students. Psychological Reports, 112(2), 519–528. doi: 10.2466/02.07.PR0.112.2.519-528CrossRefGoogle ScholarPubMed
Schennach-Wolff, R., Obermeier, M., Seemuller, F., Jager, M., Messer, T., Laux, G., … Riedel, M. (2011). Evaluating depressive symptoms and their impact on outcome in schizophrenia applying the Calgary Depression Scale. Acta Psychiatrica Scandinavica, 123(3), 228–238. doi: 10.1111/j.1600-0447.2010.01608.xCrossRefGoogle ScholarPubMed
Seery, M. D., Holman, E. A., & Silver, R. C. (2010). Whatever does not kill us: Cumulative lifetime adversity, vulnerability, and resilience. Journal of Personality and Social Psychology, 99(6), 1025–1041. doi: 10.1037/a0021344CrossRefGoogle Scholar
Shapero, B. G., Farabaugh, A., Terechina, O., DeCross, S., Cheung, J. C., Fava, M., & Holt, D. J. (2019). Understanding the effects of emotional reactivity on depression and suicidal thoughts and behaviors: Moderating effects of childhood adversity and resilience. Journal of Affective Disorders, 245, 419–427. doi: 10.1016/j.jad.2018.11.033CrossRefGoogle ScholarPubMed
Shapiro, D. I., Marenco, S., Spoor, E. H., Egan, M. F., Weinberger, D. R., & Gold, J. M. (2009). The premorbid adjustment scale as a measure of developmental compromise in patients with schizophrenia and their healthy siblings. Schizophrenia Research, 112(1-3), 136–142. doi: 10.1016/j.schres.2009.04.007CrossRefGoogle ScholarPubMed
Shrivastava, A., De Sousa, A., & Lodha, P. (2019). Resilience as a psychopathological construct for psychiatric disorders. Advances in Experimental Medicine and Biology, 1192, 479–489. doi: 10.1007/978-981-32-9721-0_23CrossRefGoogle ScholarPubMed
Simeon, D., Yehuda, R., Cunill, R., Knutelska, M., Putnam, F. W., & Smith, L. M. (2007). Factors associated with resilience in healthy adults. Psychoneuroendocrinology, 32(8-10), 1149–1152. doi: 10.1016/j.psyneuen.2007.08.005CrossRefGoogle ScholarPubMed
Skrove, M., Romundstad, P., & Indredavik, M. S. (2013). Resilience, lifestyle and symptoms of anxiety and depression in adolescence: The Young-HUNT study. Social Psychiatry and Psychiatric Epidemiology, 48(3), 407–416. doi: 10.1007/s00127-012-0561-2CrossRefGoogle ScholarPubMed
Smith, B., Fowler, D. G., Freeman, D., Bebbington, P., Bashforth, H., Garety, P., … Kuipers, E. (2006). Emotion and psychosis: Links between depression, self-esteem, negative schematic beliefs and delusions and hallucinations. Schizophrenia Research, 86(1-3), 181–188. doi: 10.1016/j.schres.2006.06.018CrossRefGoogle ScholarPubMed
Smith, P. R. (2009). Resilience: Resistance factor for depressive symptom. Journal of Psychiatric and Mental Health Nursing, 16(9), 829–837. doi: 10.1111/j.1365-2850.2009.01463.xCrossRefGoogle ScholarPubMed
Southwick, S. M., Sippel, L., Krystal, J., Charney, D., Mayes, L., & Pietrzak, R. (2016). Why are some individuals more resilient than others: The role of social support. World Psychiatry, 15(1), 77–79. doi: 10.1002/wps.20282CrossRefGoogle ScholarPubMed
Tait, L., Birchwood, M., & Trower, P. (2002). A new scale (SES) to measure engagement with community mental health services. Journal of Mental Health, 11(2), 191–198. doi: 10.1080/09638230020023570-2CrossRefGoogle ScholarPubMed
Tait, L., Birchwood, M., & Trower, P. (2004). Adapting to the challenge of psychosis: Personal resilience and the use of sealing-over (avoidant) coping strategies. The British Journal of Psychiatry, 185, 410–415. doi: 10.1192/bjp.185.5.410CrossRefGoogle Scholar
Tandon, R., Nasrallah, H. A., & Keshavan, M. S. (2009). Schizophrenia, “just the facts” 4. Clinical features and conceptualization. Schizophrenia Research, 110(1–3), 1–23. doi: 10.1016/j.schres.2009.03.005CrossRefGoogle Scholar
Torgalsboen, A. K. (2012). Sustaining full recovery in schizophrenia after 15 years: Does resilience matter? Clinical Schizophrenia & Related Psychoses, 5(4), 193–200. doi: 10.3371/CSRP.5.4.3CrossRefGoogle ScholarPubMed
Ungar, M., & Theron, L. (2020). Resilience and mental health: How multisystemic processes contribute to positive outcomes. The Lancet. Psychiatry, 7(5), 441–448. doi: 10.1016/S2215-0366(19)30434-1CrossRefGoogle ScholarPubMed
Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7, 91. doi: 10.1186/1471-2105-7-91CrossRefGoogle ScholarPubMed
Ventura, J., Subotnik, K. L., Guzik, L. H., Hellemann, G. S., Gitlin, M. J., Wood, R. C., & Nuechterlein, K. H. (2011). Remission and recovery during the first outpatient year of the early course of schizophrenia. Schizophrenia Research, 132(1), 18–23. doi: 10.1016/j.schres.2011.06.025CrossRefGoogle ScholarPubMed
Wallwork, R. S., Fortgang, R., Hashimoto, R., Weinberger, D. R., & Dickinson, D. (2012). Searching for a consensus five-factor model of the positive and negative syndrome scale for schizophrenia. Schizophrenia Research, 137(1-3), 246–250. doi: 10.1016/j.schres.2012.01.031CrossRefGoogle ScholarPubMed
Windle, G., Bennett, K. M., & Noyes, J. (2011). A methodological review of resilience measurement scales. Health and Quality of Life Outcomes, 9, 8. doi: 10.1186/1477-7525-9-8CrossRefGoogle ScholarPubMed
Zizolfi, D., Poloni, N., Caselli, I., Ielmini, M., Lucca, G., Diurni, M., … Callegari, C. (2019). Resilience and recovery style: A retrospective study on associations among personal resources, symptoms, neurocognition, quality of life and psychosocial functioning in psychotic patients. Psychology Research and Behavior Management, 12, 385–395. doi: 10.2147/PRBM.S205424CrossRefGoogle Scholar
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