Twelve-month employment intervention outcomes for drug-involved offenders.
25 Apr 2014-American Journal of Drug and Alcohol Abuse (Taylor & Francis)-Vol. 40, Iss: 3, pp 200-205
TL;DR: The efficacy of an innovative employment intervention tailored for drug-involved offenders is demonstrated by showing positive changes in 12-month employment outcomes, most strongly for those who have not had recent employment success.
Abstract: Background: Employment has been identified as an important part of substance abuse treatment and is a predictor of treatment retention, treatment completion, and decreased relapse. Although employment interventions have been designed for substance abusers, few interventions have focused specifically on drug-involved offenders. Objectives: The purpose of this study was to examine employment outcomes for drug-involved offenders who received a tailored employment intervention. Methods: In a randomized controlled trial, baseline and follow-up data were collected from 500 drug-involved offenders who were enrolled in a drug court program. Participants were randomly assigned to drug court as usual (control group) or to the employment intervention in addition to drug court. Results: Intent-to-treat analyses found that the tailored intervention was associated only with more days of paid employment at follow-up (210.1 vs. 199.9 days). When focusing on those with greater employment assistance needs, a work t...
Citations
More filters
TL;DR: Examination of posttreatment substance use among adults who have a history of exposure to violence and sought treatment for opioid use disorder shows that there is a relationship between Exposure to violence, SUDs, and relapse among patients seeking treatment.
Abstract: Exposure to violence can lead to a dramatic increase in the likelihood of the development of a substance use disorder (SUD). Given the overlap between the two, substance use for survivors of violen...
3 citations
01 Jan 2013
TL;DR: In this article, the authors examined whether rehabilitation compliance mediates the relationship between deterrence-related variables (conviction celerity and punishment severity) and DUI offender offender recidivism.
Abstract: OF DISSERTATION CONVICTION CELERITY, PUNISHMENT SEVERITY, AND TREATMENT COMPLIANCE AS PREDICTORS OF DUI RECIDIVISM: MEDIATION AND MODERATION MODELS OF DETERRENCE Driving under the influence (DUI) is one of the most frequently committed offenses in the United States and approximately one-third of DUI offenders are recidivists. Researchers have evaluated multiple DUI prevention approaches, most of which have been rooted in deterrence theory. Recently, the criminal justice system has moved away from deterrence-based approaches and begun employing various forms of rehabilitation to reduce DUI recidivism. This shift in the criminal justice system has lead researchers to begin exploring the effects of rehabilitation on DUI offenders, including an examination of offender compliance with rehabilitation programs. Although each of these areas has been investigated separately, existing studies have not incorporated deterrencerelated measures, rehabilitation compliance, and offender recidivism into a single model. Utilizing a statewide sample of Kentucky DUI offenders, the primary goal of this dissertation was to examine whether rehabilitation compliance mediates the relationship between deterrence-related variables (conviction celerity and punishment severity) and DUI offender recidivism. Second, because existing studies have produced inconclusive or mixed results regarding deterrence among DUI offenders, analyses were conducted to examine the potential moderating effects of age, gender, substance use problem severity, and location on the relationship between deterrence-related variables and DUI recidivism. Overall, the hypothesized mediation models were unsupported. There was no direct correlation between the deterrence-related variables and DUI recidivism. In addition, while there was some evidence of moderation, the hypothesized moderation models were also largely unsupported. Despite these results, compliance was significantly related to DUI recidivism in all four models, and there was evidence of relationships between both compliance and DUI recidivism with age, gender, problem severity, and location. Findings highlight the importance of compliance and social and environmental variables in predicting DUI recidivism, suggesting that these variables may be more accurate predictors of DUI recidivism than deterrence-based variables. Results demonstrate a need for the criminal justice system to place more emphasis on offenders’ treatment needs, treatment accessibility, and retention of DUI offenders in rehabilitation programs in order to decrease DUI recidivism.
3 citations
02 Mar 2016
TL;DR: In this article, the authors examined indicators of 12-month post-treatment rearrest for male criminal justice-involved substance use treatment patients and found that demographic risk factors, such as age and unemployment, were associated with significant increases in the probability of experiencing an arrest within 12-months of treatment discharge.
Abstract: Purpose – Effective substance use treatment is a viable way to reduce criminal justice contact among drug-involved offenders, but there is still a lot to learn about which indicators have the greatest impact on treatment outcomes. The purpose of this paper is to determine which clinical indicators influenced the likelihood of rearrest among male drug-involved offenders. Design/methodology/approach – This prospective longitudinal study examined indicators of 12-month post-treatment rearrest for male criminal justice-involved substance use treatment patients. Multinomial logistic regression results drawn from a sample of 1,531 adult male patients who were mandated to substance use treatment indicated that there were different factors related to the likelihood of one as well as multiple post-treatment arrests. Findings – Demographic risk factors, such as age and unemployment, were associated with significant increases in the probability of experiencing an arrest within 12-months of treatment discharge. Subst...
3 citations
TL;DR: Certain risk factors contribute to relapse, which increases risk for rearrest, and services specifically tailored to women who were court mandated to enter treatment need to consider certain demographic risk factors, clinical substance use severity, and relapse prevention as key elements to minimize subsequent criminal offending.
Abstract: Background and objectives
Many women who experience substance dependence come into contact with the criminal justice system and are mandated by the court to enter treatment. Treatment is a viable option and can have many positive outcomes, but there remains significant room for improvement. This study was designed to identify key risk factors that can be addressed to improve substance use treatment outcomes for this population.
Methods
The study sample consisted of (n) 381 women who were court mandated to enter substance use treatment. Multivariate path analyses were conducted to assess the associations between correlates of substance use treatment outcomes, risk for relapse, and rearrest.
Results
Women who displayed certain demographic risk factors (ie, less educated and unmarried) and had greater levels of substance use severity prior to entering treatment experienced elevated risk for relapse. Consequently, women who relapsed were nearly three times (OR = 2.50, 95%CI = 1.26–4.93) as likely to be rearrested within 12 months of discharge from treatment compared to those who did not relapse.
Discussion and Conclusions
Certain risk factors contribute to relapse, which increases risk for rearrest.
Scientific Significance
Services specifically tailored to women who were court mandated to enter treatment need to consider certain demographic risk factors, clinical substance use severity, and relapse prevention as key elements to minimize subsequent criminal offending. (Am J Addict 2015;24:495–498)
2 citations
References
More filters
Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.
14,604 citations
TL;DR: The use of the ASI is suggested to match patients with treatments and to promote greater comparability of research findings, suggesting the treatment problems of patients are not necessarily related to the severity of their chemical abuse.
Abstract: The Addiction Severity Index (ASI) is a structured clinical interview developed to fill the need for a reliable, valid, and standardized diagnostic and evaluative instrument in the field of alcohol and drug abuse. The ASI may be administered by a technician in 20 to 30 minutes producing 10-point problem severity ratings in each of six areas commonly affected by addiction. Analyses of these problem severity ratings on 524 male veteran alcoholics and drug addicts showed them to be highly reliable and valid. Correlational analyses using the severity ratings indicated considerable independence between the problem areas, suggesting that the treatment problems of patients are not necessarily related to the severity of their chemical abuse. Cluster analyses using these ratings revealed the presence of six subgroups having distinctly different patterns of treatment problems. The authors suggest the use of the ASI to match patients with treatments and to promote greater comparability of research findings.
3,143 citations
639 citations
"Twelve-month employment interventio..." refers background in this paper
...(20) reported that only those clients in long-term treatment showed improvement in employment status as compared to clients in methadone maintenance treatment....
[...]
TL;DR: In this article, a meta-analysis of 154 independent evaluations of adult drug courts, 34 of juvenile drug courts and 28 of DWI drug courts was carried out to systematically review quasi-experimental and experimental evaluations of the effectiveness of drug courts.
344 citations
"Twelve-month employment interventio..." refers background in this paper
...There is mounting evidence that drug courts are effective in reducing recidivism (33), and interventions focusing on areas that relate to treatment retention and outcomes, such as employment, may further reduce drug use and criminality....
[...]