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Record W4410217046 · doi:10.1002/gps.70093

Facility‐Level Variation of Resident Loneliness in Assisted Living and Associated Organizational Context Factors: A Repeated Cross‐Sectional Study

2025· article· en· W4410217046 on OpenAlexafffundabout
Matthias Hoben, Hana Dampf, Rashmi Devkota, Kyle Corbett, David B. Hogan, Kimberlyn McGrail, Colleen J. Maxwell

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsYork UniversityUniversity of British ColumbiaUniversity of WaterlooInstitute for Clinical Evaluative SciencesUniversity of CalgaryUniversity of Alberta
FundersFaculty of Nursing, University of AlbertaCanadian Institutes of Health ResearchAlberta InnovatesUniversity of Alberta
KeywordsLonelinessGeeCross-sectional studyMedicineGerontologyContext (archaeology)DemographyPopulationGeneralized estimating equationPandemicPsychologyEnvironmental healthCoronavirus disease 2019 (COVID-19)PsychiatryGeographyDisease

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Loneliness is common among nursing home residents, and it is also thought to be a problem in assisted living (AL). However, we lack research on loneliness in AL. Our objectives were to assess changes in risk-adjusted prevalence of loneliness in AL, and facility-level variations in loneliness before and during the COVID-19 pandemic, and facility-level factors associated with AL resident loneliness during the pandemic. RESEARCH DESIGN AND METHODS: This population-based, repeated cross-sectional study used Resident Assessment Instrument-Home Care (RAI-HC) data (01/2017-12/2021) from Alberta, Canada. On a system-level, we estimated quarterly, risk-adjusted loneliness prevalence, and used segmented regressions to assess whether loneliness changed after the start of the pandemic. For risk adjustment, we used resident-covariates known to be associated with loneliness, but out the health system's or AL home's control (e.g., age or cognitive impairment) to enable fair comparisons over time. Linking AL home surveys, collected in COVID-19 waves 1 (March-June 2020) and 2 (October 2020-February 2021) to RAI-HC records, we used covariate-adjusted general estimating equations (GEE) to assess AL home factors (e.g., staffing shortages, social distancing measures) associated with resident-level loneliness during the pandemic. RESULTS: Quarterly samples included 2026-2721 residents. Loneliness [95% confidence interval] fluctuated between 13.6% [11.5%-15.7%], and 16.8% [14.4%-19.2%], with no statistically significant increase during the pandemic. Facility-level median [inter-quartile range] loneliness prevalence varied considerably before (14.9% [8.3%-21.1%) and during the pandemic (13.5% [6.9%-21.3%]). GEEs included 985 residents in 41 facilities (wave 1), and 1134 residents in 42 facilities (wave 2). Facility-factors associated with decreased odds of loneliness included: facilitating caregiver involvement (odds ratio = 0.531 [95% confidence interval: 0.286-0.986]), essential visitor policies (0.672 [0.454-0.994]), and video calls with volunteers or religious/spiritual leaders (0.603 [0.435-0.836]). Facilitating outdoor activities/visits (2.486 [1.561-3.961], and providing hallway-based activities (1.645 [1.183-2.288]) were associated with increased odds of loneliness. DISCUSSION AND IMPLICATIONS: Loneliness did not change during COVID-19 in AL on a health system level, but varied considerably between facilities before and during the pandemic. Modifiable facility-level factors explained variations in loneliness within facilities, suggesting important targets for policies and improvement interventions.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.159
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.390
Teacher spread0.351 · 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".

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Citations2
Published2025
Admission routes3
Has abstractyes

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