Understanding the Impact of COVID-19 on People Who Use Drugs in Sacramento County
Bibliographic record
Abstract
During the COVID-19 outbreak, there was a surge in overdose-related deaths with the CDC estimating a 38.4% increase in opioid-related deaths and a 26.5% increase in cocaine-related deaths during June 2019-May 2020, when compared to the 12 months prior1. With subsequent surveillance data confirming this trend, it is evident that COVID-19 had a unique impact on people who use drugs (PWUD). However, the psychosocial, socioeconomic, and biopolitical effects of the pandemic on PWUD are poorly understood. The COVID-19 pandemic resulted in disruptions to illicit drug supplies in Canada, leading to increased use of contaminated and variably potent substances2. With social distancing guidelines in place, syringe service programs were deemed non-essential in many areas, effectively cutting off the supply of safe injection materials while contaminated drugs circulated. Given this background, it is necessary to better understand the ways COVID-19 has impacted the health and behaviors of PWUD. By engaging directly with PWUD in Sacramento County, this mixed-methods project aims to go to the source and address this important gap in knowledge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".