Characteristics associated with forced treatment discontinuation among people who use drugs in Rhode Island
Bibliographic record
Abstract
BACKGROUND: A barrier to remaining in substance use treatment in the U.S. is abstinence-based policies, which can lead to premature treatment termination. In this study, we examine factors associated with forced treatment discontinuation due to substance use among participants in the Rhode Island Prescription and Illicit Drug Study (RAPIDS). METHODS: We examined baseline data from RAPIDS participants (who enrolled between August 2020 and February 2023) and who reported any history of enrollment in substance use treatment programs. Modified Poisson regression was used to assess sociodemographic, drug use behavior, and clinical characteristics associated with forced treatment discontinuation due to substance use. The type of treatment from which participants were removed and the drug(s) that were implicated in the removal were also summarized. RESULTS: Among 406 eligible participants, 96 (24 %) experienced forced treatment discontinuation due to drug or alcohol use. The most frequently reported program from which participants were removed was residential treatment (75 %). The most frequently reported drug that was implicated in the removal was crack cocaine (33 %). In the modified Poisson analysis, regular use of powder cocaine was associated with experiencing forced treatment discontinuation (adjusted prevalence ratio = 1.50; 95 % confidence interval: 1.05, 2.15). CONCLUSIONS: Forced treatment discontinuation due to substance use is common, particularly in residential drug treatment. Our findings suggest a need for enhanced access to evidence-based treatments for stimulant use disorders. Persisting racial/ethnic disparities in treatment access underscores the need for strategies to ensure racial and ethnic equity in substance use treatment access and retention in care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".