The Effectiveness of Incarceration‐Based Drug Treatment on Criminal Behavior*
Bibliographic record
Abstract
This Campbell Review evaluates the effects of four different approaches to drug abuse treatment for incarcerated offenders in relation to criminal behaviour and relapse into drug abuse. It also examines what characterises the effective programmes. The findings of this research review are based on a meta-analysis of 66 independent assessments. These are part of 53 studies which include more than 165,000 offenders (one assessment included more than 95,000 offenders). 58 studies were carried out in the USA, three in Australia, three in Canada, one in Taiwan and one in the UK. The treatment of incarcerated drug abusers can reduce recidivism by up to 20% . However, there are major differences in how the various types of treatment work, both with regard to avoiding relapse into crime and continued drug abuse. Therapeutic communities have a positive effect on both criminal behaviour and drug abuse. Counselling programmes only reduce recidivism, but do not appear to be equally effective for all types of offenders. Other types of treatment – narcotic maintenance programmes (e.g. methadone treatment) and boot camps – do not appear to reduce recidivism. This research review emphasises the need for more insight into which specific parts of a treatment programme are the most important. It is the conclusion of this review that future research should be based on the application of the strictest requirements for the chosen assessment design.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".