An Overview of Class Actions and Covid-19 in Ontario’s Long-Term Care Facilities
Bibliographic record
Abstract
Abstract: This article examines the complex considerations that confront those pursuing class action lawsuits against Ontario’s long-term care (LTC) homes in response to the COVID-19 pandemic’s devastating impact on the residents of those facilities and their families. I present an overview of the contexts and circumstances in which Ontario’s LTC facilities responded to the pandemic. The discussion of several critical issues associated with the operation of LTC residences is intended to provide information about the appalling conditions in those facilities and the Ontario government’s concomitant failures, while also identifying some of the obstacles confronting plaintiffs. The interaction of historical, political, economic, and social considerations, associated with the operation of Ontario’s LTC homes, attests to the complexity of the contexts and circumstances in which the duty and standard of care in those facilities, during a pandemic, must be assessed. The challenges involved in establishing causation with respect to COVID-19 deaths in LTC facilities are integrally related to the “complex, multifaceted, and polycentric” * The Public Inquiry into the Safety and Security of Residents in the Long-Term Care Homes System (Toronto: Ministry of the Attorney General, 2019), online: https://longtermcareinquiry.ca/en/final-report/ . nature of well-documented, enduring systemic problems, as well as residents’ age and frailty. The paper examines Ontario’s recent controversial legislative initiatives in terms of the issues presented by the adoption of “made-in-America” certification tests, and the retroactive imposition of a prohibitive standard of negligence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".