Volume 26, Issue 117 (9-2018)                   zumsj 2018, 26(117): 21-31 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

S E P, S M, A M, Z E, M D. The Efficacy of Matrix Model Treatment in the Reduction of Addiction Severity and Relapse Prevention among Amphetamine Abusers . zumsj. 2018; 26 (117) :21-31
URL: http://zums.ac.ir/journal/article-1-5402-en.html
Abstract:   (1559 Views)
Background and Objective: Amphetamine abuse has become a major problem in recent years. The aim of this study was to examine the efficacy of Matrix Model treatment in  amphetamine abusers.
Materials and Methods: This study was a clinical trial with a pretest-posttest design. The study population included all known abusers of amphetamines in Zanjan, Iran.The sample consisted of 40 people referring to local psychiatric and psychological clinics. They were chosen based on convenient sampling and were randomly assigned into two groups, pharmacotherapy and combined therapy (pharmacotherapy and Matrix Model treatment). ASI questionnaires and urinary tests were administered before and after treatment and the data were analyzed using ANCOVA and chi-squared tests.
Results: The treatment was effective when considering job status, drug and alcohol abuse, salary, family and mental status (p= 0/001). In terms of the medical status of addiction severity, there was no significant difference between the two groups. Chi test showed that the frequency of positive urinary tests decreased in the combined therapy group (p= 0/05).
Conclusion: Results suggest that the Matrix Model Treatment can reduce addiction severity and can also reduce the frequency of positive urinary tests.

Full-Text [PDF 3028 kb]   (550 Downloads)    
Type of Study: Original | Subject: General
Received: 2018/11/10 | Accepted: 2018/11/10 | Published: 2018/11/10

Add your comments about this article : Your username or Email:

© 2019 All Rights Reserved | ZUMS Journal

Designed & Developed by : Yektaweb