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Record W3038903334 · doi:10.1080/01490400.2020.1786753

Residential Mobility, Social Leisure Activity, and Depressive Symptoms among Chinese Middle-Aged and Older Adults: A Longitudinal Analysis

2020· article· en· W3038903334 on OpenAlexaff
Jiaying Lyu, Huan Huang, Liang Hu, Lin Yang

Bibliographic record

VenueLeisure Sciences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersNational Natural Science Foundation of China
KeywordsLongitudinal studyChinaDepression (economics)GerontologyUrbanizationSocial supportDepressive symptomsPsychologyLeisure activityLongitudinal dataMedicineDemographyGeographyCognitionPsychiatrySociologyEconomic growth

Abstract

fetched live from OpenAlex

The residential mobility of Chinese middle-aged and older adults is rising due to the continuous advancement of urbanization, economic growth, and the continuation of intergenerational support. Residential mobility, as a source of social disruption, has been associated with depression among adults in developed countries. However, the impact of residential mobility on depression in later life remains underexplored in China. The current study retrieved data from the China Health and Retirement Longitudinal Study (2011, 2013, and 2015 waves), a nationally representative longitudinal survey on adults of 45 years and above. Panel regression models revealed that residential mobility was significantly associated with depressive symptoms in Chinese middle-aged and older adults, and such association was moderated by social leisure activity participation. The current study highlights the protective role of social leisure activity in the moving-depression relationship.

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 score0.046
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.329
Teacher spread0.301 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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