A rapid review of the impacts of “Big Events ” on risks, harms, and service delivery among people who use drugs: Implications for responding to COVID-19
Bibliographic record
Abstract
BACKGROUND: "Big Events" are major disruptions to physical, political, and economic environments that can influence vulnerability to drug-related harms. We reviewed the impacts of Big Events with relevance to the COVID-19 pandemic on drug-related risk and harms and access to drug treatment and harm reduction services. METHODS: We conducted a rapid review of quantitative, qualitative, and mixed methods literature relating to the following Big Events: respiratory infection pandemics, natural disasters, financial crises, and heroin shortages. Included studies reported data on changes to risks, harms, and/or service provisioning for people who use illicit drugs (other than cannabis) in the context of these Big Events. Searches were conducted in PubMed in May 2020, and two reviewers screened studies for inclusion. Peer-reviewed studies published in English or French were included. We used a narrative synthesis approach and mapped risk pathways identified in the literature. RESULTS: No studies reporting on respiratory infection pandemics were identified. Twelve studies reporting on natural disaster outcomes noted marked disruption to drug markets, increased violence and risk of drug-related harm, and significant barriers to service provision caused by infrastructure damage. Five studies of the 2008 global financial crisis indicated increases in the frequency of drug use and associated harms as incomes and service funding declined. Finally, 17 studies of heroin shortages noted increases in heroin price and adulteration, potentiating drug substitutions and risk behaviors, as well as growing demand for drug treatment. CONCLUSION: Current evidence reveals numerous risk pathways and service impacts emanating from Big Events. Risk pathway maps derived from this literature provide groundwork for future research and policy analyses, including in the context of the COVID-19 pandemic. In light of the findings, we recommend responding to the pandemic with legislative and financial support for the flexible delivery of harm reduction services, opioid agonist treatment, and mental health care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".