Coronavirus Disease 2019 (COVID-19) and Ontario’s Long-term Care Homes
Bibliographic record
Abstract
Ontario long-term care home residents have experienced disproportionately high morbidity and mortality, both from COVID-19 and from the conditions associated with the COVID-19 pandemic. As of March 16, 2022, a total of 4,286 long-term care home residents have died of COVID-19, totaling 43.0% of all 9,245 COVID-19 deaths in Ontario. The most important risk factors for whether a long-term care home will experience an outbreak is the daily incidence of SARS-CoV-2 infections in the communities surrounding the home and the occurrence of staff infections. The most important risk factors for the extent of an outbreak and the number of resulting resident deaths are older home design, chain ownership, and crowding. Many Ontario long-term care home residents have experienced severe and potentially irreversible physical, cognitive, psychological, and functional declines as a result of precautionary public health interventions imposed on homes, such as limiting access to general visitors and essential caregivers, resident absences, and group activities. There has also been an increase in the prescribing of psychoactive drugs to Ontario long-term care residents during the pandemic. The accumulating evidence on COVID-19 in Ontario’s long-term care homes has been leveraged in several ways to support public health interventions and policy during the pandemic. Several further measures could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s long-term care homes. This includes improving staffing, minimizing long-term care worker infection, reducing crowding in long-term care homes, enhanced infection prevention and control (IPAC) measures, a more balanced and nuanced approach to public health measures, and additional strategies to promote COVID-19 vaccine acceptance amongst residents and staff.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".