Treating Opioid Use Disorders in Drug Court: Participants’ Views on Using Medication-Assisted Treatments (MATs) to Support Recovery
Bibliographic record
Abstract
Drug courts began in 1989 in Miami-Dade County, FL. Due to their success in treating substance use disorders and reducing criminal recidivism, they have expanded globally and are currently operating in countries such as Australia, Canada, and Scotland, to name a few. Drug courts can be a key intervention in addressing the opioid epidemic. This is the first known qualitative study to ask drug court participants ( n = 38) who have opioid use disorders questions related to their lived experiences in drug court, as well as direct questions related to the use of medication-assisted treatments (MATs) in drug court. Overall, drug court participants felt that MATs were helpful for treating their opioid use disorders; however, some participants reported using other drugs while on MATs and they viewed their recovery through a harm reduction lens. Additionally, participants emphasized the importance of using MATs in combination with counseling that used cognitive and behavioral therapies. Implications for drug court practice and future research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".