804. Infection Control and Clinical Support in Long-Term Care Homes During the COVID-19 Pandemic in North Toronto: A Quasi-Experimental Study
Bibliographic record
Abstract
Abstract Background Wave one of the COVID-19 pandemic in Ontario, Canada, resulted in significant institutional outbreaks associated with high case fatality among older adults. Our hospital formally partnered with congregate care homes in north Toronto to support infection control and clinical management before wave two of the COVID-19 pandemic. Our aim was to evaluate the impact of this program on resident and healthcare worker (HCW) outcomes. Methods A multicentre quasi-experimental study was conducted comparing outcomes between wave one (March-June, 2020) and wave two (October-December, 2020) among 17 congregate care homes (4 long term care homes and 13 residential homes). During wave two, weekly meetings and 42 on-site visits were conducted along with on-site daily hospital presence for all COVID-19 outbreaks to support infection control and resident management. The primary outcomes included COVID-19-case fatality rate as well as overall resident fatality including COVID-19 and non-COVID-19 related causes. Secondary outcomes included healthcare worker COVID-19 infections, and infection control practices among homes with paired audits (n=6), including hand hygiene, use of personal protective equipment, environmental cleaning and physical distancing practices. Results Among 2203 residents during wave one and 2287 residents during wave two, there was reduction in COVID-19 case fatality rate (38.1% vs. 13.4%; p< 0.01), overall COVID-19-related fatality (2.3% vs. 1.0%; p< 0.01) and non COVID-19 related fatality (8.3% vs. 3.5%; p< 0.01). Weekly staff testing and increased syndromic surveillance was implemented during wave two. Among 2590 staff, there were 2.6% vs.4.2% staff who tested positive for COVID-19 during wave one and two, respectively. Changes in infection control practice were observed in regard to directly observed hand hygiene (83.3% vs. 100%), use of personal protective equipment (16.7% vs. 83.3%), environmental cleaning (66.7% vs. 100%) and physical distancing (66.7% vs. 83.3%). Conclusion Integration of hospital with community congregate care homes was associated with improvements in resident outcomes during wave two of the pandemic. Further longitudinal support and evaluation is needed to ensure sustainability. Disclosures All Authors: No reported disclosures
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".