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Record W4386090606 · doi:10.1186/s12877-023-04196-3

Social isolation and loneliness among older adults living in rural areas during the COVID-19 pandemic: a scoping review

2023· review· en· W4386090606 on OpenAlexafffund
John Pickering, Andrew Wister, Eireann O’Dea, Habib Chaudhury

Bibliographic record

VenueBMC Geriatrics · 2023
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsLonelinessSocial isolationPandemicThematic analysisMedicineIsolation (microbiology)Psychological interventionGerontologyRural areaQualitative researchCoronavirus disease 2019 (COVID-19)NursingSociologyDiseasePsychiatrySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: The causes and consequences of social isolation and loneliness of older people living in rural contexts during the COVID-19 pandemic were systematically reviewed to describe patterns, causes and consequences. METHODS: Using the Arksey and O'Malley (2005) scoping review method, searches were conducted between March and December 2022, 1013 articles were screened and 29 were identified for data extraction. RESULTS: Findings were summarized using thematic analysis separated into four major themes: prevalence of social isolation and loneliness; rural-only research; comparative urban-rural research; and technological and other interventions. Core factors for each of these themes describe the experiences of older people during the COVID-19 pandemic and related lockdowns. We observed that there are interrelationships and some contradictory findings among the themes. CONCLUSIONS: Social isolation and loneliness are associated with a wide variety of health problems and challenges, highlighting the need for further research. This scoping review systematically identified several important insights into existing knowledge from the experiences of older people living in rural areas during the COVID-19 pandemic, while pointing to pressing knowledge and policy gaps that can be addressed in future research.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.171
GPT teacher head0.475
Teacher spread0.304 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations35
Published2023
Admission routes2
Has abstractyes

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