The COVID-19 pandemic and the right to health of people who use drugs
Bibliographic record
Abstract
Context: According to the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) people who inject drugs (PWID) make up a significant part of the population that needs to be looked after to eliminate infectious diseases such as those caused by HIV and HCV. This situation has been exacerbated by the COVID-19 pandemic. As COVID-19 rapidly spread across the globe, governments implemented various prevention measures which not only caused an increase in problem drug use (PDU) because of their negative impact on mental health and socioeconomic conditions but also prompted a decrease in drug services provided. Therefore, new challenges appeared, such as increased demand for drugs and diversification of clients, and new needs. Nevertheless, in clear contradiction to what was needed, the EMCDDA’s initial reports suggested that there was a decline in European drug services both in providing treatment and harm reduction interventions. COVID-19 increased the need to access drug services, healthcare, and support services creating an increased demand for opioid substitution therapy and other medication. Thus, comprehensive, and sustainable policies are needed to combat the public health threats associated with these challenges and to ensure the continuity of care. Policy Options: The challenging circumstances brought by the COVID-19 pandemic require policymakers need to take action to build capacity and resiliency for those facing drug-related health and social problems. These should include the adoption of integrated strategies that combine drug consumption rooms, substance-specific therapies, provision of free needles and naloxone, primary healthcare, and social support. Recommendations: The creation of an integrated drug policy framework addressed to European Union member states is necessary to create robust drug services capable of surviving a crisis. This is guided by a relevant policy design and implementation framework, alongside tangible action principles in line with low-threshold service provision.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| 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".