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Record W4323536213 · doi:10.14745/ccdr.v49i23a06

Integration of hospital with congregate care homes in response to the COVID-19 pandemic

2023· article· en· W4323536213 on OpenAlexaffvenueabout
Christina K. Chan, Mercedes Magaz, Victoria Williams, Julie M. Wong, Monica Klein-Nouri, Sid Feldman, Jaclyn O'Brien, Natasha Salt, Andrew E. Simor, Jocelyn Charles, Brian M. Wong, Steve Shadowitz, Karen G. Fleming, Adrienne K. Chan, Jerome A. Leis

Bibliographic record

VenueCanada Communicable Disease Report · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsApotex (Canada)University of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsPandemicMedicineContext (archaeology)Coronavirus disease 2019 (COVID-19)OutbreakInfection controlTransmission (telecommunications)Long-term careEmergency medicineMedical emergencyFamily medicineNursingDiseaseIntensive care medicineInternal medicineInfectious disease (medical specialty)VirologyGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.381
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes3
Has abstractyes

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