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Record W2908699027 · doi:10.1080/13607863.2018.1479836

Everyday solitude, affective experiences, and well-being in old age: the role of culture versus immigration

2019· article· en· W2908699027 on OpenAlexaffabout
Da Jiang, Helene H. Fung, Jennifer C. Lay, Maureen C. Ashe, Peter Graf, Christiane A. Hoppmann

Bibliographic record

VenueAging & Mental Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSolitudeLonelinessAffect (linguistics)ImmigrationChinaPsychologyDemographySocial psychologySociologyHistoryPsychiatry

Abstract

fetched live from OpenAlex

Objectives: Being alone is often equated with loneliness. Yet, recent findings suggest that the objective state of being alone (i.e. solitude) can have both positive and negative connotations. The present research aimed to examine (1) affective experience in daily solitude; and (2) the association between everyday affect in solitude and well-being. We examined the distinct roles of culture and immigration in moderating these associations.Method: Using up to 35 daily life assessments of momentary affect, solitude, and emotional well-being in two samples (Canada and China), the study compared older adults who aged in place (local Caucasians in Vancouver , Canada and local Hong Kong Chinese in Hong Kong, China) and older adults of different cultural heritages who immigrated to Canada (immigrated Caucasians and immigrated East Asians).Results: We found that older adults of East Asian heritage experienced more positive and less negative affect when alone than did Caucasians. Reporting positive affect in solitude was more positively associated with well-being in older adults who had immigrated to Canada as compared to those who had aged in place.Conclusions:These findings speak to the unique effects of culture and immigration on the affective correlates of solitude and their associations with well-being in old age.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.983

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.326
Teacher spread0.318 · 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.

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

Citations44
Published2019
Admission routes2
Has abstractyes

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