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Record W4401550015 · doi:10.1177/00914150241268089

Penalty Versus Premium: Social Disposition Differentiates Life Satisfaction Among Living-Alone Immigrant and Native-Born Older Adults—Findings From the Canadian Longitudinal Study on Aging (CLSA)

2024· article· en· W4401550015 on OpenAlexaffabout
Jing Shen, Hongmei Tong, Esme Fuller‐Thomson

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute for Work & HealthMacEwan UniversityUniversity of Toronto
Fundersnot available
KeywordsLife satisfactionPsychologyImmigrationLongitudinal studyIndependence (probability theory)Big Five personality traitsDevelopmental psychologyGerontologyDemographyPersonalitySocial psychologyMedicineSociologyGeography

Abstract

fetched live from OpenAlex

Using data from the Canadian Longitudinal Study on Aging, in this study we provide an alternative explanation for the gap of life satisfaction between living-alone immigrants and Canadian-born older adults. Based on the Big-Five personality traits, we use the latent class analysis to generate two types of social dispositions, social independence and social dependence. With social dispositions taken into account, living alone contributes to life satisfaction in opposite ways for immigrant and Canadian-born older adults, by playing a negative role for the former group and a positive role for the latter. The trend of higher life satisfaction among the living-alone Canadian-born are mainly among the socially independent, whereas for immigrants, socially dependent older adults experience the lowest level of life satisfaction when living alone. Therefore, while socially independent Canadian-born older adults gain a "living-alone premium" in life satisfaction; their socially dependent immigrant counterparts experience a "living-alone penalty" in life satisfaction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.343
Teacher spread0.300 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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