Risk and protective factors for opioid overdose during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction People who use drugs (PWUD) are now at the intersection of two public health emergencies – the Covid-19 pandemic and the overdose crisis. They may be at heightened risk of overdose due to increased isolation, worsened mental health, and changes to the illicit drug supply. The province of British Columbia (BC) in Canada is anticipated to experience a record-breaking year of overdose deaths as over 1,500 people (32.9 deaths per 100,000) have died from overdose in 2020. In response, BC released new clinical guidelines in March to allow the prescribing of pharmaceutical alternatives aiming to reduce PWUD’s risk of overdose and contracting Covid-19. Objectives We examined the risk and protective factors for overdose during these dual crises. We explored how the Covid-19 pandemic has impacted the mental health and substance use of PWUD and their access to treatment and harm reduction services. Methods We are conducting a survey among patients with opioid use disorder at a major hospital in Vancouver, BC. It includes the following domains: sociodemographic characteristics; mental and physical health; substance use patterns; opioid overdose history; access to treatment, harm reduction services; impacts of Covid-19. Results We anticipate collecting data from 200 participants. Descriptive statistics and regression analysis will be conducted to describe the sample and determine the risk, protective factors for overdose. Conclusions We will gain a better understanding of overdose risk in PWUD who are now navigating the complex challenges created by the dual crises. This will in turn inform the establishment of evidence-based strategies to reduce their overdose risk. Disclosure No significant relationships.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".