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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".