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Record W4200061914 · doi:10.1093/geroni/igab046.1664

Social Isolation and Aging Out of Place Among Immigrants and Refugee Seniors in Canada

2021· article· en· W4200061914 on OpenAlexaffabout
Shanthi Johnson, Juanita-Dawne Bacsu, Tom McIntosh, Bonnie Jeffery, Nuelle Novik

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of ReginaUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsRefugeeImmigrationConceptualizationSocial isolationIsolation (microbiology)Context (archaeology)Government (linguistics)PrioritizationPolitical scienceEconomic growthSociologyPsychologyGeographyBusiness

Abstract

fetched live from OpenAlex

Abstract Immigrant and refugee seniors experience cultural barriers, discrimination, and limited networks which increase the risk of social isolation and thus hinder their active participation in the society. This paper explores social isolation among immigrant and refugee seniors in Canada based on an environmental scan of federal/provincial/territorial and community-based programs and a systematic scoping review. Findings revealed important gaps and regional disparities in opportuntiies to reduce social isolation and great active participation. Research was limited, often qualitative in nature, typically based on larger urban centres, with measurement issues related to the need for consideration beyond one's living arrangements. The results highlight the need for greater understanding Canada’s immigration and refugee system and policies, and collaboration across levels of government. Reducing issues of social isolation and enabling better active aging for vulnerable seniors require a more nuanced and multidimensional conceptualization with prioritization on addressing the unique factors of culture and geographical context.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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 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
Published2021
Admission routes2
Has abstractyes

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