Investigating the conceptual model of the formation of psychological resilience in farmers affected by droughts in Iran using structural equation modeling

IntroductionDrought has had a significant impact on farmers, resulting in a substantial increase in mental health disorders among them. This study aimed to test the model of forming psychological resilience among farmers in drought-affected regions in Iran using a valid and reliable tool designed during the research.MethodA quantitative cross-sectional study was conducted in 2024 in two stages. Firstly, the tool for measuring the model components was designed. Its psychometric properties were evaluated using the content and face validity as well as Cronbach's alpha and test-retest methods. Secondly, the model was tested using structural equation modeling (SEM). The sample size was estimated as 400 farmers being selected by the cluster-random sampling method. Descriptive statistics were performed using SPSS version 22.0 and SEM was conducted using Mplus version 7.ResultsOut of 400 distributed questionnaires, 387 were returned to the researchers. The psychometric results showed acceptable validity and reliability (CVI = 0.86, CVR = 0.92, and Cronbach's alpha = 0.88). The SEM results indicated that the initial model needed modifications to improve fit indices (chi(2)/df = 2.30, CFI = 0.67, TLI = 0.77, RMSEA = 0.042, SRMR = 0.031). The valid model for psychological resilience was achieved after conducting the modifications (chi(2)/df = 2.90, CFI = 0.98, TLI = 0.97, RMSEA = 0.031, SRMR = 0.021).ConclusionThis study showed that the model is appropriate for the context of Iran. Utilizing the model and its components, targeted interventions can be implemented to improve the psychological resilience of farmers both in practice and in the field. Furthermore, the tool designed in this study allows for the analysis of the model's applicability in other provinces affected by drought in Iran. Based on the findings, informed decisions and interventions can be effectively formulated.