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Record W4408397284 · doi:10.1080/09687599.2025.2478058

Seeking redress for harm in institutional care during the COVID-19 pandemic: immunity from civil liability as barrier

2025· article· en· W4408397284 on OpenAlexafffundabout
Poland Lai

Bibliographic record

VenueDisability & Society · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRedressHarmPandemicCoronavirus disease 2019 (COVID-19)LiabilityPolitical science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CriminologyLaw and economicsVirologyLawMedicinePsychologySociology

Abstract

fetched live from OpenAlex

The harms in institutions such as psychiatric facilities and long-term care (LTC) homes (aged care or nursing homes) uncovered during the COVID-19 pandemic accelerated momentum for deinstitutionalisation. In 2022, the United Nations Committee on the Rights of Persons with Disabilities adopted the Guidelines on Deinstitutionalisation, Including in Emergencies. Section 9 of the Guidelines covers remedies, reparations, and redress. This paper examines a development that hinders redress for older disabled people who lived in LTC homes. Some jurisdictions passed legislation which provides a level of protection from civil liability in relation to actions of governments and other parties during the pandemic. Drawing from legal developments in Ontario (Canada), this paper argues that providing broad civil liability protection to LTC homes and governments normalizes and trivializes wrongs committed by institutions and governments during the pandemic. This paper calls for greater legal accountability over experiences of harm in LTC as integral to deinstitutionalization.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.467
Teacher spread0.367 · 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 designObservational
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

Citations0
Published2025
Admission routes3
Has abstractyes

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