Take-home dosing experiences among persons receiving methadone maintenance treatment during COVID-19
Bibliographic record
Abstract
PURPOSE: Methadone maintenance treatment is a life-saving treatment for people with opioid use disorders (OUD). The coronavirus pandemic (COVID-19) has introduced many concerns surrounding access to opioid treatment. In March 2020, the Substance Abuse and Mental Health Services Administration (SAMHSA) issued guidance allowing for the expansion of take-home methadone doses. We sought to describe changes to treatment experiences from the perspective of persons receiving methadone at outpatient treatment facilities for OUD. METHODS: We conducted an in-person survey among 104 persons receiving methadone from three clinics in central North Carolina in June and July 2020. Surveys collected information on demographic characteristics, methadone treatment history, and experiences with take-home methadone doses in the context of COVID-19 (i.e., before and since March 2020). RESULTS: Before COVID-19, the clinic-level percent of participants receiving any amount of days' supply of take-home doses at each clinic ranged from 56% to 82%, while it ranged from 78% to 100% since COVID-19. The clinic-level percent of participants receiving a take-homes days' supply of a week or longer (i.e., ≥6 days) since COVID-19 ranged from 11% to 56%. Among 87 participants who received take-homes since COVID-19, only four reported selling their take-home doses. CONCLUSIONS: Our study found variation in experiences of take-home dosing by clinic and little diversion of take-home doses. While SAMSHA guidance should allow expanded access to take-home doses, adoption of these guidelines may vary at the clinic level. The adoption of these policies should be explored further, particularly in the context of benefits to patients seeking OUD treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".