Integration of substance use disorder services with primary care: health center surveys and qualitative interviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Each year, nearly 20 million Americans with alcohol or illicit drug dependence do not receive treatment. The Affordable Care Act and parity laws are expected to result in increased access to treatment through integration of substance use disorder (SUD) services with primary care. However, relatively little research exists on the integration of SUD services into primary care settings. Our goal was to assess SUD service integration in California primary care settings and to identify the practice and policy facilitators and barriers encountered by providers who have attempted to integrate these services. METHODS: Primary survey and qualitative interview data were collected from the population of federally qualified health centers (FQHCs) in five California counties known to be engaged in SUD integration efforts was surveyed. From among the organizations that responded to the survey (78% response rate), four were purposively sampled based on their level of integration. Interviews were conducted with management, staff, and patients (n=18) from these organizations to collect further qualitative information on the barriers and facilitators of integration. RESULTS: Compared to mental health services, there was a trend for SUD services to be less integrated with primary care, and SUD services were rated significantly less effective. The perceived difference in effectiveness appeared to be due to provider training. Policy suggestions included expanding the SUD workforce that can bill Medicaid, allowing same-day billing of two services, facilitating easier reimbursement for medications, developing the workforce, and increasing community SUD specialty care capacity. CONCLUSIONS: Efforts to integrate SUD services with primary care face significant barriers, many of which arise at the policy level and are addressable.
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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 it