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Record W4416453446 · doi:10.1177/14680173251369661

It is Not Only About Staying Alive: A Living Systematic Review of Social Care Strategies for Older Adults in Residential Care Facilities During the COVID-19 Pandemic

2025· article· en· W4416453446 on OpenAlexafffundabout
Anna Azulai, Beverly Michel Baluyot, Alison Pitcher, Sejla Catovic

Bibliographic record

VenueJournal of Social Work · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMacEwan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychosocialResidential carePsychological interventionPandemicSocial distanceSocial isolationSocial careGeriatric careLong-term care

Abstract

fetched live from OpenAlex

Summary: The COVID-19 pandemic has had a negative impact on older adults in geriatric residential care facilities worldwide. In Canada, the mortality rate in these care settings was particularly high. To curb the spread of the virus, social distancing and other restrictions were introduced with inadvertent negative impacts on psychosocial well-being of residents in facilities. The goal of this Living Systematic Review (LSR) was to synthesize emerging evidence on social care strategies in Canadian residential care facilities during the COVID-19 pandemic. Findings: Results from the 35 studies show that the inclusion of older adults from residential care in research samples has been rare. Also, most of the identified social care interventions were on micro-, meso-, and exolevels, whereas macrolevel focus was limited. Applications: For the profound systemic impact of macropolicies, future studies should evaluate macrolevel interventions to ensure high quality of geriatric residential care in Canada. Also, future research should amplify the voices of older adults living in these care settings. Finally, greater attention to social care is required in Canadian geriatric residential care facilities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.411
Teacher spread0.374 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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