Prisons, drugs and COVID-19: Early releases and continuity of care
Bibliographic record
Abstract
Abstract Introduction Detention settings are high-risk environments for the spread of infectious diseases. Since 2020, COVID-19 has posed unprecedented challenges for governments and prison administrations. In some jurisdictions, this has catalysed early release programmes to decongest prisons to minimise the harm of COVID-19 in prison systems. Methods From March to June 2020, HRI monitored the adoption of prison decongestion measures in response to COVID-19 in Europe and worldwide. HRI tracked criteria for eligibility and implementation of the measures and distributed online expert surveys as part of the Global State of Harm Reduction 2020 that included questions on harm reduction in prisons and the response to COVID-19. Survey data was supplemented by a review of academic, governmental, and non-governmental literature. A review to update the data will be carried out by mid-2021. Results and discussion Results show prison decongestion schemes initiated in 17 countries in Europe and 109 countries worldwide. Overall, by July 2020 decongestion measures reduced the global prison population by 16% in Europe and just 6% worldwide. In a quarter of countries (including at least four in Europe), people incarcerated for drug offences were explicitly excluded, regardless of whether they suffered from health condition or belong to a vulnerable group. We found no evidence of expanded access to harm reduction programmes to address the risk of overdose after release. Issues that exacerbate overdose risk included interruptions to the provision of opioid agonist therapy (OAT) and the unavailability in most jurisdictions of naloxone on release. Conclusions People who use drugs and are in detention settings have been inadequately served during the COVID-19 pandemic. To address the unique health risks of detention settings, there is a need for greater commitment to the adoption of non-custodial measures, and diversion from criminal justice towards a health-led response to drug use.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".