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Measuring Coping Among Family Members with Substance-Misusing Relatives: Testing Competing Factor Structures of the Coping Questionnaire (CQ) in England and Italy.

Zsolt HorváthJim OrfordRichard VellemanRóbert Urbán
Published in: Substance use & misuse (2019)
Background: The Coping Questionnaire measures affected family members' responses to their relatives' substance misuse related problems. The Coping Questionnaire examines three main coping strategies: engaged, tolerant-inactive, and withdrawal coping. Objectives: The aim of the current study was to compare competing conceptual measurement models across two countries, including one-factor, three-factor, and higher order factor models. Methods: Secondary analysis of data from five previous studies was conducted. Samples of affected family members from England (N = 323) and Italy (N = 165) were aggregated into two country specific groups. Series of confirmatory factor analyses were performed to test the degree of model fit and the effects of socio-demographic variables on the coping factors. Results: A bifactor model fitted the data most closely relative to the one- and three-factor models. High rates of common variance (60-65%) were attributable to the general coping factor, while a high proportion of the variance related to the withdrawal coping subscale score was independent (66-89%) of the general coping factor. Family members' country, age, gender, the type of relationship and the main problematic substance had significant effects on the coping factors. Conclusions: A bifactor model related to coping behaviors is consistent with the theoretical assumptions of the general coping literature. The concept of a general coping factor also fits the theoretical assumptions of the stress-strain-coping-support model, with family members showing a general tendency to cope with the harmful circumstances which arise due to substance misuse.
Keyphrases
  • social support
  • depressive symptoms
  • systematic review
  • mental health
  • cross sectional
  • chronic pain
  • machine learning
  • electronic health record
  • artificial intelligence
  • psychometric properties