Your browser doesn't support javascript.
loading
The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors.
Lunansky, Gabriela; van Borkulo, Claudia D; Haslbeck, Jonas M B; van der Linden, Max A; Garay, Cristian J; Etchevers, Martín J; Borsboom, Denny.
Afiliación
  • Lunansky G; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands.
  • van Borkulo CD; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands.
  • Haslbeck JMB; Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands.
  • van der Linden MA; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands.
  • Garay CJ; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands.
  • Etchevers MJ; Faculty of Psychology, University of Buenos Aires, Buenos Aires, Argentina.
  • Borsboom D; Faculty of Psychology, University of Buenos Aires, Buenos Aires, Argentina.
Front Psychiatry ; 12: 640658, 2021.
Article en En | MEDLINE | ID: mdl-33815173
Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Psychiatry Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Psychiatry Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Suiza