Monthly Trends of Substance Use Among Mainers Receiving Buprenorphine Treatment During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: Drug-related deaths in Maine increased by 23% in the first quarter of 2020 compared to the last quarter of 2019. Most of these deaths were accidental overdoses involving at least one opioid, and 65% of these deaths were caused by fentanyl, according to the Maine Center for Disease Control and Prevention. Methods: This research explored substance use in Maine during 2020. Among the sample of individuals, 46% were homeless and receiving recovery services at a buprenorphine-assisted treatment program at a federally qualified health center in Maine. Charts of 35 patients were reviewed for emergency room visits and urine drug screens. Results: In the sample, 20% of individuals screened positive for fentanyl, 22% screened positive for methamphetamines, and 20% screened positive for cocaine. In the first month after lockdown, the presence of fentanyl and methamphetamines in urine drug screens doubled compared to before the lockdown. In the months after lockdown, the amounts of fentanyl and methamphetamines in drug screens and the number of emergency room visits increased. Discussion: Examples of Maine’s harm-reduction strategies are discussed. These results highlight the urgency to implement more drastic measures statewide, especially among individuals who are homeless and have an opioid and/or a stimulant use disorder. Conclusion: Greater recovery services are required for individuals who are homeless and have a substance use disorder in the aftermath of the pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".