Management of COVID-19 Response in a Secure Forensic Mental Health Setting: Réponse à la gestion de la COVID-19 dans un établissement sécurisé de santé mentale et de psychiatrie légale
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic presents major challenges to places of detention, including secure forensic hospitals. International guidance presents a range of approaches to assist in decreasing the risk of COVID-19 outbreaks as well as responses to manage outbreaks of infection should they occur. METHODS: We conducted a literature search on pandemic or outbreak management in forensic mental health settings, including gray literature sources, from 2000 to April 2020. We describe the evolution of a COVID-19 outbreak in our own facility, and the design, and staffing of a forensic isolation unit. RESULTS: We found a range of useful guidance but no published experience of implementing these approaches. We experienced outbreaks of COVID-19 on two secure forensic units with 13 patients and 10 staff becoming positive. One patient died. The outbreaks lasted for 41 days on each unit from declaration to resolution. We describe the approaches taken to reduction of infection risk, social distancing and changes to the care delivery model. CONCLUSIONS: Forensic secure settings present major challenges as some proposals for pandemic management such as decarceration or early release are not possible, and facilities may present challenges to achieve sustained social distancing. Assertive testing, cohorting, and isolation units are appropriate responses to these challenges.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".