Substance use disorders and COVID-19: reflections on international research and practice changes during the “poly-crisis”
Bibliographic record
Abstract
Since March 2020, the COVID-19 pandemic has had a disproportionately high toll on vulnerable populations, coinciding with increased prevalence of alcohol-and drug-related deaths and pre-existing societal issues such as rising income inequality and homelessness. This poly-crisis has posed unique challenges to service delivery for people with substance use disorders, and innovative approaches have emerged. In this Perspectives paper we reflect on the poly-crisis and the changes to research and practice for those experiencing substance use disorders, following work undertaken as part of the InterGLAM project (part of the 2022. Lisbon Addictions conference). The authors, who were part of an InterGLAM working group, identified a range of creative and novel responses by gathering information from conference attendees about COVID-19-related changes to substance use disorder treatment in their countries. In this paper we describe these responses across a range of countries, focusing on changes to telehealth, provision of medications for opioid use disorder and alcohol harm reduction, as well as changes to how research was conducted. Implications include better equity in access to technology and secure data systems; increased prescribed safer supply in countries where this currently does not exist; flexible provision of medication for opioid use disorder; scale up of alcohol harm reduction for people with alcohol use disorders; greater involvement of people with lived/living experience in research; and additional support for research in low- and middle-income countries. The COVID-19 pandemic has changed the addictions field and there are lessons for ongoing and emerging crises.
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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 | 0.115 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.025 | 0.047 |
| Insufficient payload (model declined to judge) | 0.012 | 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".