The Structural Equation Modeling of Relapse Prediction based on Internal Locus of Control and Risk Perception with the Mediation role of Dependent Beliefs in Men Substance-Dependent Undergoing Quitting

Document Type : Original Article

Authors

1 Ph.D Candidate in General Psychology, Semnan Branch, Islamic Azad University, Semnan, Iran.

2 Corresponding author, Associate Professor, Department of Psychology, Semnan Branch, Islamic Azad University, Semnan, Iran.

3 Assistant Professor of Health psychology, Department of Psychiatry and Psychosomatic Eesearch Center, Mazandaran University of Medical Sciences, Sari, Iran.

Abstract

Background: The purpose of the present study was to investigate structural equation modeling of relapse prediction based on internal locus of control and risk perception with the mediation role of dependent beliefs in men substance-dependent undergoing quitting.
Method: This was descriptive-correlation research with structural equations modeling. The statistical population of the study was men substance-dependent undergoing quitting who of Semnan city in autum 2023 to spring year 2024. The sample size was selected based on Cochran and with subjectivw sampling of 386 men. Data collection tools include the relapse prediction scale (PRS) of Wright and et al (1993), locus of control scale (LCS) of Rotter (1966), risk perception index (RPI) of Benthin et al (1993) and dependent beliefs questionnaire (DBQ) of Beck and Whright (1993). Data were analyzed using Pearson correlation and structural equations modeling.
Results: The results showed direct effect of internal locus of control (β= - 0.61 and sig=0.001) and risk perception (β= - 0.71 and sig=0.001) on relapse prediction are significant. The results also showed that dependent beliefs has a significant mediating role in the relationship between internal locus of control (β= - 0.66 and sig=0.001) and risk perception (β = - 0.57 and sig = 0.001) with relapse prediction. Also, the final research model had a good fit (RMSEA=0.06, SRMR=0.04, p<0.05) and 89% of the variance of relapse prediction is explained.

Keywords

Main Subjects

References

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  • Receive Date: 13 August 2024
  • Revise Date: 12 March 2025
  • Accept Date: 03 May 2025

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