Using recovery management checkups for primary care to improve linkage to alcohol and other drug use treatment: a randomized controlled trial three month findings
Bibliographic record
Abstract
BACKGROUND AND AIMS: Recovery management checkups (RMC) have established efficacy for linking patients to substance use disorder (SUD) treatment. This study tested whether using RMC in combination with screening, brief intervention, and referral to treatment (SBIRT), versus SBIRT alone, can improve linkage of primary care patients referred to SUD treatment. DESIGN: A randomized controlled trial of SBIRT as usual (n = 132) versus SBIRT plus recovery management checkups for primary care (RMC-PC) (n = 134) with follow-up assessments at 3 months post-baseline. SETTING: Four federally qualified health centers in the United States serving low-income populations. PARTICIPANTS: Primary care patients (n = 266, 64% male, 80% Black, mean age, 48.3 [range, 19-53]) who were referred to SUD treatment after SBIRT. INTERVENTIONS: SBIRT alone (control condition) compared with SBIRT + RMC-PC (experimental condition). MEASUREMENT: The primary outcome was any days of SUD treatment in the past 3 months. Key secondary outcomes were days of SUD treatment overall and by level of care, days of alcohol and other drug (AOD) abstinence, and days of using specific substances, all based on self-report. FINDINGS: At 3-month follow-up, those assigned to SBIRT + RMC-PC (n = 134) had higher odds of receiving any SUD treatment (46% vs 20%; adjusted odds ratio = 4.50 [2.49, 8.48]) compared with SBIRT only, including higher rates of entering residential and intensive outpatient treatment. They also reported more days of treatment (14.45, vs 7.13; d = +0.26), more days abstinent (41.3 vs 31.9; d = +0.22), and fewer days of using alcohol (27.14, vs 36.31; d = -0.25) and cannabis (19.49, vs 28.6; d = -0.20). CONCLUSIONS: Recovery management checkups in combination with screening, brief intervention, and referral to treatment are an effective strategy for improving linkage of primary care patients in need to substance use disorder treatment over 3 months.
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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 | 0.000 |
| 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) | 0.000 | 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".