Detecting drug addict’s personal characteristics that define the resocialization dynamics

Keywords: drug addiction, drug addicted personality, remission, narcotic substances, psychoactive drug, drug prevention.

Abstract

A drug addicted person is in a depressed state, and experiences severe emotions and suffering. Our research's relevance lies in the study of individual personality traits that predispose to the formation of long-term stable remission. The study aims to determine the personal characteristics of drug addicts, based on which it is possible to predict the course of the resocialization process and the dynamics of the course of the state of remission. The multifactorial personality questionnaire MMPI in modification 4.7 BMS (computer version) was used as the main diagnostic tool for the personality traits of a drug addict. We selected 67 indicators for discriminant analysis out of 90 personality traits diagnosed by the MMPI questionnaire. The total sample of this study was 309 people. The main experimental group, hereinafter group I, consisted of 214 people with a diagnosis of opium addiction stages I-II. The average experience of using narcotic substances was 8.2 years, aged 19 to 36 years, with remission of 12 days. Group II - 38 respondents with remission from 1 to 3 years, Group III - 12 respondents with remission for more than 3 years. Control group IV, 45 people, consisted of subjects without a history of opium addiction diagnosis. The result obtained is presented as a spatial classification of the four surveyed groups' subjects according to the results of canonical discriminant functions. As a result, we obtained a diagnostic algorithm based on the discriminant analysis in drug-addicted subjects, which allows, based on consideration of nine personal characteristics, to classify a subject as a drug addict and predict his movement in the resocialization process.

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Published
2020-08-31
How to Cite
Mostova, T. (2020). Detecting drug addict’s personal characteristics that define the resocialization dynamics. Herald of Kiev Institute of Business and Technology, 44(2), 64-72. https://doi.org/10.37203/kibit.2020.44.09