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Record W3093389474 · doi:10.1177/2333721420962669

Maintaining Resident Social Connections During COVID-19: Considerations for Long-Term Care

2020· article· en· W3093389474 on OpenAlexaffabout
Carla Ickert, Heather Rozak, Jennifer Masek, Keeley Eigner, Sherry Schaefer

Bibliographic record

VenueGerontology and Geriatric Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsCapitalCare Foundation
Fundersnot available
KeywordsLong-term carePandemicCoronavirus disease 2019 (COVID-19)Phone2019-20 coronavirus outbreakGerontologyPublic healthBusinessWindow of opportunityMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Infection controlMedical emergencyEnvironmental healthNursingInfectious disease (medical specialty)DiseaseIntensive care medicineComputer scienceOutbreak

Abstract

fetched live from OpenAlex

Worldwide, long-term care (LTC) homes have been heavily impacted by the coronavirus disease 2019 (COVID-19) pandemic. The significant risk of COVID-19 to LTC residents has resulted in major public health restrictions placed on LTC visitation. This article describes the important considerations for the facilitation of social connections between LTC residents and their loved ones during the COVID-19 pandemic, based on the experiences of 10 continuing care homes in Alberta, Canada. Important considerations include: technology, physical space, human resource requirements, scheduling and organization, and infection prevention and control. We describe some of the challenges encountered when implementing alternative visit approaches such as video and phone visits, window visits and outdoor in-person visits, and share several strategies and approaches to managing this new process within LTC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.115
GPT teacher head0.433
Teacher spread0.317 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations31
Published2020
Admission routes2
Has abstractyes

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