COVID-related disruptions and adaptations to prison-based mental health and substance use services: a narrative review
Bibliographic record
Abstract
PURPOSE: Public health experts and advocates have long raised concerns about the pandemic preparedness of prison systems worldwide - an issue that became increasingly salient at the start of the COVID-19 pandemic. People in prison experience poorer health outcomes compared to the general population, making timely access to adequate health services in prison critical for their health and wellbeing. This study aims to identify the extent of the literature on initial changes in mental health and substance use services for people in prison during the COVID-19 pandemic, summarize and synthesize the findings and identify areas in need of further study. DESIGN/METHODOLOGY/APPROACH: The authors conducted a review of the academic literature published internationally in English between 2019 and December 1, 2020 to describe the disruptions and adaptations to mental health and substance use services in prisons during the onset of the COVID-19 pandemic. FINDINGS: The authors found that mental health and substance use services in prisons around the world were widely disrupted due to the COVID-19 pandemic - predominantly consisting of the complete suspension of services, discontinuation of transfers to off-site treatment sites and limitations on service capacity. Adaptations ranged from virtual service delivery and changes to treatment dispensation processes to information sessions on overdose prevention. ORIGINALITY/VALUE: To the best of the authors' knowledge, this is the first review to examine the nature and extent of the literature on delivery of mental health and substance use services in prisons during the COVID-19 pandemic.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".