Integration of hospital with congregate care homes in response to the COVID-19 pandemic
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to improve the safety of the environments where we care for older adults in Canada.After providing assistance during the first wave, many Ontario hospitals formally partnered with local congregate care homes in a "hub and spoke" model during second pandemic wave onward.The objective of this article is to describe the implementation and longitudinal outcomes of residents in one hub and spoke model composed of a hospital partnered with 18 congregate care homes including four long-term care and 14 retirement or other congregate care homes.Intervention: Homes were provided continuous seven-day per week access to hospital support, including infection prevention and control (IPAC), testing, vaccine delivery and clinical support as needed.Any COVID-19 exposure or transmission triggered a same-day meeting to implement initial control measures.A minimum of weekly on-site visits occurred for long-term care homes and biweekly for other congregate care homes, with up to daily on-site presence during outbreaks.Outcomes: Case detection among residents increased following implementation in context of increased testing, then decreased post-immunization until the Omicron wave when it peaked.After adjusting for the correlation within homes, COVID-related mortality decreased following implementation (OR=0.51,95% CI, 0.30-0.88;p=0.01).In secondary analysis, homes without pre-existing IPAC programs had higher baseline COVID-related mortality rate (OR=19.19,95% CI, 4.66-79.02;p<0.001) and saw a larger overall decrease during implementation (3.76% to 0.37%-0.98%)as compared to homes with pre-existing IPAC programs (0.21% to 0.57%-0.90%). Conclusion:The outcomes for older adults residing in congregate care homes improved steadily throughout the COVID-19 pandemic.While this finding is multifactorial, integration with a local hospital partner supported key interventions known to protect residents.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".