Disruption of Opioid Treatment Program Services Due to an Extreme Weather Event: An Example of Climate Change Effects on the Health of Persons Who Use Drugs
Bibliographic record
Abstract
Climate change and the opioid epidemic in combination may pose significant challenges for individuals with opioid use disorder due to potential disruptions in access to essential addiction treatment services caused by extreme weather events. Despite concerns over the escalating health impacts of climate change, limited research has documented and explored the vulnerability of patients enrolled in opioid treatment programs to disruptions caused by climate change and particularly extreme cold events. In this commentary, we describe the impact of a catastrophic flooding event during record-setting cold temperatures at an opioid treatment program in Seattle, WA. By examining this event, we highlight the potential vulnerabilities the methadone treatment infrastructure faces regarding climate change and future extreme weather events. In doing so, we hope to draw attention to a critical need for research that describes, plans for, and addresses disruptions to opioid use disorder treatment resulting from climate change-related weather events.
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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) | 0.001 | 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 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".