Evaluating the role of Section 1115 waivers on Medicaid coverage and utilization of opioid agonist therapy among substance use treatment admissions
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of Section 1115 waivers on Medicaid coverage and opioid agonist therapy (OAT) utilization among substance use treatment admissions. DATA SOURCE: Treatment Episode Data Set-Admissions (TEDS-A) (2001-2012). STUDY DESIGN: We examined effects of 1115 waiver implementation on proportions of substance use treatment admissions with Medicaid and receiving OAT, using random intercept linear regression. PRINCIPAL FINDINGS: 1115 waiver implementation was associated with an average of a 6 percentage point increase in proportion of all admissions with Medicaid, and 4 percentage point increase among opioid outpatient admissions. Implementation was not associated with change in proportion of opioid outpatient admissions receiving OAT. CONCLUSIONS: 1115 waivers influence Medicaid coverage among substance use treatment admissions. The findings improve our understanding of how state policies impact substance use treatment utilization.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".