Seeking redress for harm in institutional care during the COVID-19 pandemic: immunity from civil liability as barrier
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".