The Age-Dependent Value of Life: An Analysis of State Responses to COVID-19 in Ontario’s Long-Term Care Homes from March 2020-December 2020
Bibliographic record
Abstract
During the early waves of the COVID-19 pandemic, Canada saw mass infection among its long-term care (LTC) residents, leading to that vulnerable population dying disproportionately of the virus. Canada’s failed responses to preventing and controlling outbreaks in LTC facilities resulted in the country experiencing the highest death toll among wealthy nations during the first wave. During this time, Canada’s LTC residents accounted for 78.4% of the country’s overall deaths, while the OECD 12-country average was 47.3%. Resident fatalities in Canada were approximately 50% higher than in Spain, Italy, the United States, and the United Kingdom (Akhtar-Danesh et al., 2022, p. 2; Sepulveda et al., 2020, p. 1572). The pattern of mass death among LTC residents persists into the present due to government inaction and repeated mistakes. Notably, the province of Ontario continues to fare poorly, recording a total of 5,044 resident deaths and 13 staff deaths by July 1, 2022, due to COVID-19, and similar conditions to those experienced during the pandemic’s earlier waves (NIA Long-Term Care COVID-19 Tracker, 2022). Throughout the pandemic, outbreaks in Ontario’s facilities saw residents die in deplorable conditions documented by members of the Canadian Armed Forces (CAF) who were deployed to some of the hardest-hit homes during the Spring of 2020. This article is a timely counter to the Ontario government’s responses to COVID-19 in LTC. By analyzing legislation and public documents, it identifies how the state protected corporate LTC interests and the neoliberal status quo in elder care. It uncovers how dominant discourses naturalizing a neoliberal model of care weaponized ageist discourses to justify the mistreatment of LTC residents. It highlights the role of the state in LTC and presents the case of Ontario as a unique example of failed responses.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".