Perspectives on the COVID-19 Pandemic Response in a Forensic Psychiatric Hospital: Informing Future Planning
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in rapid and unprecedented public policy and legislative interventions to reduce the global spread of the virus. The scope of these challenges has been particularly broad and pressing within health care settings. Individuals with severe mental illness hospitalized in psychiatric facilities are at greater risk of infection than the general population due to both the characteristics of the population (e.g., mentally ill individuals may find the physical distancing measures difficult to understand) and the nature of the settings (e.g., communal living, frequent admissions and discharges). Therefore, it is essential that preventative measures are taken to minimize the chance of nosocomial outbreak in long-term psychiatric facilities; yet minimal information specific to forensic contexts is available. This paper reviews the system-wide strategies that have been put in place across a large Canadian forensic facility and offers recommendations on how to respond to a pandemic or other outbreak in a secure psychiatric setting. Taking the response to COVID-19 in the context of a forensic psychiatric setting, we discuss a wide range of essential aspects of pandemic planning and provide examples of innovative practices that should be considered for retention, future research and broader implementation.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".