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Record W2930814343 · doi:10.3390/su11072090

The Effects of Co-Residence on the Subjective Well-Being of Older Chinese Parents

2019· article· en· W2930814343 on OpenAlexaff
Shanwen Zhu, Man Li, Renyao Zhong, Peter C. Coyte

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
FundersPeking UniversityNational Natural Science Foundation of China
KeywordsResidenceLonelinessPsychologyHappinessFeelingChinaDemographyOptimismLife satisfactionDevelopmental psychologyGerontologySocial psychologyMedicineGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effects of co-residence on the parental subjective well-being among older Chinese parents. Our analysis included 2968 elderly parents. Parental subjective well-being was stratified into positive well-being (PWB) and negative well-being (NWB). Positive well-being was assessed through questions about life satisfaction, optimism, and happiness and NWB was measured by questions about fear, loneliness, and feelings of uselessness. We found co-residence with adult children resulted in a significant average increase in PWB by 0.17 points relative to those who did not cohabitate. In rural China, co-residence with adult children significantly increased PWB by 0.19 points, and co-residence with a son significantly increased parental PWB by 0.18 points. Negative well-being fell significantly by 0.63 points if co-residence was with an adult daughter. Our findings imply that support from adult children significantly improved parental PWB, especially for the elderly in rural China. Public policies that facilitate the strengthening of cohabitation may help improve the well-being for older Chinese residents. Our study makes two main contributions to the international literature: first, we strengthened the causal inferences regarding the effects of co-residence with adult children on parental well-being through the use of a longitudinal study design; and second, we introduced a difference-in-differences propensity score matching (PSM-DID) approach to address potential selection bias that has previously been ignored in the literature.

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)0.004
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.279
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

Citations7
Published2019
Admission routes1
Has abstractyes

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