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Record W2102445627 · doi:10.1017/s0144686x13000470

Immigration and loneliness in later life

2013· article· en· W2102445627 on OpenAlexaffabout
Zheng Wu, Margaret J. Penning

Bibliographic record

VenueAgeing and Society · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLonelinessImmigrationEthnic groupResidencePopulationDemographyPsychologyGerontologyMedicineSociologyGeographySocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Although the loneliness of both older adults and immigrants is frequently asserted, knowledge regarding the implications of immigration for loneliness in later life is limited. In particular, little attention has been directed to the impact of factors that might differentiate individuals within the immigrant population. Using data from the 2007 General Social Survey (GSS-21) conducted by Statistics Canada, this study examined the effects of immigrant status as well as immigrant generation, length of residence in Canada and race/ethnicity on loneliness among adults aged 60 and over (N=10,553). Regression analyses (ordinary least squares) estimating both the general and age-specific effects of immigrant experience on loneliness, indicated that immigrants report higher levels of loneliness than native-born Canadians, that race/ethnicity influenced loneliness particularly among immigrants and that generational status as well as length of residence also had an impact, but one that differed across age groups. Immigration-related variables appeared less consequential for loneliness in the oldest-old (aged 80+) than in younger elderly age groups. These findings attest to the significance of immigrant status for an understanding of loneliness in later life but suggest a need to acknowledge the diversity of immigrant experiences associated with lifecourse and other factors.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.294
Teacher spread0.277 · 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".

Quick stats

Citations138
Published2013
Admission routes2
Has abstractyes

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