Substance Use During the COVID-19 Pandemic in the Americas: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, substance use in the Americas was influenced by various factors, including social isolation, increased stress, and disruption of healthcare services. While the impacts varied across populations, the health crisis exacerbated challenges related to substance use, particularly in more vulnerable groups. This article aims to describe the pandemic's impact on substance use and substance use disorders in the Americas region. METHODS: We conducted a scoping review across 4 databases (Pubmed, Scielo, Lilacs, and Google Scholar) using a search paradigm based on the combination of several keywords related to substance use during the COVID-19 pandemic in the Americas. RESULTS: Most studies were conducted at the beginning of the pandemic and carried out in the United States and Canada. A higher proportion of the general population decreased or showed no changes in cannabis consumption. On the other hand, for those who reported pre-pandemic substance use, consumption has increased as a strategy to cope with COVID-related stress, exacerbating preexisting problems. Patients with substance use disorders reduced their visits to treatment services and in-person medication visits declined significantly. In an attempt to reverse the distancing of patients from the services, there was an increase in take-home medication and telehealth services. Rates for opioid-related deaths and other substance overdose-related deaths increased during the pandemic, especially among racial/ethnic minorities. CONCLUSION: The study concludes that the pandemic-intensified substance use among vulnerable populations, particularly individuals with pre-existing mental health conditions or a history of substance use disorders, while having a low impact on the general population. This divergence has contributed to widening health disparities.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".