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
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".