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Record W7038587013

The intersection between crime and drug dependence: Establishing the clinical utility of the Severity of Dependence Scale (SDS) with a sample of federally incarcerated, male offenders

2009· article· en· W7038587013 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Ecology and Taxonomy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationSample (material)Scale (ratio)Logistic regressionMatching (statistics)RecidivismSubstance abuseMeasure (data warehouse)
DOInot available

Abstract

fetched live from OpenAlex

Between 70% to 80% of Correctional Service Canada's (CSC) general offender population and over 90% of its Aboriginal offender population has an identified substance abuse problem requiring intervention. Ensuring that these offenders receive the most effective treatment is a major challenge that is best addressed through the application of assessments that are shown to be reliable, accurate, and useful for client-treatment matching and correctional planning. Aim. The main objective of the study was to establish the Severity of Dependence Scale (SDS) (Gossop et al. 1995) as a suitable measure for client-treatment matching, and as a predictor of recidivism and relapse to substance use. Setting. The SDS and the Drug Abuse Screening Test (DAST) (Skinner, 1982) were administered to a sample of 3350 adult, male inmates from CSC between 2002 and 2007. A total of 1667 inmates were eventually released from custody and available for 24 months of follow-up. Measurements. Cronbach's coefficient alpha provided a measure of internal consistency (reliability), and canonical correlation analysis quantified the dimensional relationship between the two instruments. With DAST as the reference standard, Receiver Operating Characteristics (ROC) analyses established the optimal cut-off score for a classification of psychological drug dependence on the SDS. A number of multivariable logistic regression models uncovered the dimensions of the classification, while a series of Cox proportional hazards models examined SDS's ability to predict the rates of revocation and relapse to substance abuse over a maximum of 24 months of follow-up into the community. Findings. Large Cronbach's coefficient alpha values confirmed the internal consistency of both the DAST and SDS. The canonical correlation analysis revealed 11 linear combinations of DAST and SDS items that were highly correlated along a single dimension that closely approximated the dependence syndrome as defined by the Diagnostic and Statistical Manual of Mental Disorders-IV. The results from the logistic regression and Receiver Operating Characteristics (ROC) analyses underscored the strong relationship between DAST's classification of drug dependence and the SDS. The cut-off value of ≥6 for a classification of psychological drug dependence produced the best trade-off between sensitivity and specificity. The individual logistic regression models and the significant unconditional associations between indicators within a number of life domains and psychological drug dependence uncovered a host of deficits that are important for client-treatment matching and correctional planning. The SDS was also predictive of post-release outcomes. After adjusting for the effects of other predictors within a series of Cox proportional hazards models, offenders who were classified as psychologically drug dependent had higher hazards of revocation and relapse to substance abuse. However, exposure to the high intensity program and community-based maintenance reduced the hazard of revocation and relapse to substance. Conclusions. The SDS was a reliable measure of psychological drug dependence, and useful for differentiating offenders for treatment and for predicting post-release outcomes. The findings underscore the importance of accurately matching offender criminogenic need to appropriate levels of service delivery, and reinforce the importance of community aftercare in mitigating the risk of recidivism and relapse to substance abuse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.218
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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