Medication Treatment For Opioid Use Disorders In Substance Use Treatment Facilities
Bibliographic record
Abstract
Medication treatment (MT) is one of the few evidence-based strategies proposed to combat the current opioid epidemic. We examined national trends and correlates of offering MT in substance use treatment facilities in the United States. According to data from national surveys, the proportion of these facilities that offered any MT increased from 20.0 percent in 2007 to 36.1 percent in 2016-mainly the result of increases in offering buprenorphine and extended-release naltrexone. Only 6.1 percent of facilities offered all three MT medications in 2016. Facilities in states with higher opioid overdose death rates, facilities that accepted health insurance overall (and, more specifically, those that accepted Medicaid in states that opted to expand eligibility for Medicaid), and facilities in states with more comprehensive coverage of MT under their Medicaid plans had higher odds of offering MT. The findings highlight the persistent unmet need for MT nationally and the role of expansion of health insurance in the dissemination of these treatments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".