A randomized controlled trial of recovery management checkups for primary care patients: Twelve‐month results
Bibliographic record
Abstract
BACKGROUND: Primary care settings like federally qualified health centers (FQHC) are optimal locations to identify individuals with substance use disorders (SUD) and link them to SUD treatment, yet successful linkage has proven difficult. Recovery management checkups for primary care (RMC-PC) is a promising method for increasing linkage to care, engagement in treatment, and reducing substance use. METHODS: Participants (n = 266) who received screening, brief intervention, and referral to treatment (SBIRT) at four FQHC sites and needed SUD treatment were randomized to receive SBIRT only or SBIRT+RMC-PC. All participants received SBIRT prior to randomization as part of usual care while those in the experimental group also received quarterly checkups. All participants completed research interviews at enrollment and 3, 6, 9, and 12 months post-enrollment. The primary outcome was whether participants received any days of SUD treatment. Key secondary outcomes were days of SUD treatment (total and by SUD level of care), days of alcohol or drug abstinence, and a reduction in days of specific substance use, all based on self-report. RESULTS: Relative to participants receiving SBIRT only, participants assigned to SBIRT+RMC-PC were significantly more likely to have received any SUD treatment over 12 months (adjusted odds ratio [AOR] = 3.85) and more days of SUD treatment over 12 months (Cohen's effect size d = +0.41). The SBIRT+RMC-PC group also reported significantly more days of abstinence over 12 months (d = +0.30), fewer days of alcohol use (d = -0.20) and cannabis use (d = -0.20), and lower combined substance use frequency (d = -0.25). Days of treatment were found to positively mediate the direct effect of SBIRT+RMC-PC on days of abstinence. CONCLUSION: This study provides further evidence of the effectiveness of the "referral to treatment" component of SBIRT when combined with RMC for patients in primary care settings, including those with drug use problems. Moreover, results demonstrate the value of repeated checkups on longer-term treatment and substance use outcomes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".