Pharmacological interventions for alcohol misuse in correctional settings: A systematic review
Bibliographic record
Abstract
BACKGROUND: The prevalence of alcohol use disorder (AUD) is estimated to be 10 times higher amongst individuals in the criminal justice system than the general population. Alcohol use is also one of the strongest modifiable risk factors for recidivism. One intervention that has been shown to be effective in reducing alcohol consumption in the general population is medication-assisted treatment (MAT), and this systematic review synthesized the existing evidence on MAT for AUD in correctional settings. METHODS: Empirical, peer-reviewed studies on approved medications for AUD in correctional populations were searched in major databases. One hundred sixty-two articles were initially screened and 14 eligible articles were included in the final review. Four articles examined disulfiram, and 10 articles examined naltrexone. RESULTS: The studies on disulfiram were considerably older than those on naltrexone, predating contemporary scientific standards. Disulfiram in combination with substantial contingencies in a supervised setting significantly reduced alcohol-related measures of consumption and recidivism and had acceptable safety and tolerability. All naltrexone studies showed significant reductions in alcohol-related measures, but effects on recidivism were mixed. The naltrexone studies indicated that it was highly acceptable and well tolerated. In addition, offenders receiving naltrexone had significantly greater medication adherence, treatment attendance, and treatment duration than with placebo. CONCLUSIONS: A small number of studies on pharmacological interventions for AUD in the correctional population suggest that MAT is effective in reducing alcohol consumption, although results on recidivism are mixed. On balance, the evidence was more supportive of naltrexone in reducing alcohol-related outcomes than disulfiram and it may also be a more feasible intervention in correctional settings. Further research on MAT to address AUD in correctional populations with larger sample sizes, longer duration, and in combination with behavioral interventions is warranted.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".