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Record W3112230220 · doi:10.22329/wyaj.v37i0.6565

Litigating in the Time of Coronavirus: Mental Health Tribunals’ Response to COVID-19

2020· article· en· W3112230220 on OpenAlexaffvenueabout
Ruby Dhand, Anita Szigeti, Maya Kotob, Michael Kennedy, Rebecca Ye

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of WindsorYork UniversityThompson Rivers University
Fundersnot available
KeywordsMental healthHarmPandemicEconomic JusticeCriminologyAddictionPsychiatryPopulationCoronavirus disease 2019 (COVID-19)Political sciencePsychologyMedicineLawEnvironmental health

Abstract

fetched live from OpenAlex

People with mental health and addiction issues are disproportionately affected by COVID-19 given the elevated risk of contracting COVID-19 within psychiatric facilities. The impact of the pandemic on this extraordinarily vulnerable population includes the potential for large outbreaks and multiple deaths. There is also the increased risk of serious psychological harm, exacerbating pre-existing mental health and substance use issues and in turn elevating their risk to themselves and/or others. In Part I of this paper, we analyze the procedural barriers to access to justice that arose as a result of the initial responses to COVID-19 by the Consent and Capacity Board [CCB] and the Ontario Review Board [ORB]. In Part V, we include a brief report on how appeals taken from both tribunals have been handled throughout COVID-19 to date. In Part VI, we analyze the discretionary and systemic barriers experienced by people with mental health and addiction issues appearing before the CCB and ORB during COVID-19. We critique recent mental health law cases during COVID-19 where deprivations of liberty interests and substantive equality have occurred, and access to justice for people with mental health and addictions issues has been denied, suspended or impaired. Through a legal analysis of how the pandemic has impacted this vulnerable community of litigants, we hope this research will result in further advocacy and education to prevent outbreaks and death, improve health care practices, and increase access to justice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.212
GPT teacher head0.518
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes3
Has abstractyes

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