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Record W3027155637 · doi:10.3389/fpsyg.2020.00885

Are Empty-Nest Elders Unhappy? Re-examining Chinese Empty-Nest Elders’ Subjective Well-Being Considering Social Changes

2020· article· en· W3027155637 on OpenAlexaboutno aff
Yan Zhang

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsSpouseHappinessNest (protein structural motif)ChinaPsychologyFilial pietyRural areaDemographyGerontologyGeographySocial psychologyMedicineSociologyGender studies

Abstract

fetched live from OpenAlex

Aging, the one-child policy, and migration have altered Chinese family structure and the number of empty-nest elders is increasing. Since living without children runs in the opposite direction of filial piety, empty-nest elders have typically been negatively viewed and depicted as unhappy. However, individualization and the unbalanced development of China may decrease the impact of children but increase the impact of the spouse and rural-urban gaps on elders’ well-being. Therefore, this study re-examined the subjective well-being of empty-nest elders considering these social changes. Participants (N = 765; age range = 60–94 years, Mage = 70.25 years, SDage = 7.85; men = 45%) were recruited from two large cities, two small cities, and two rural areas in China. Elders’ subjective well-being was measured by the Memorial University of Newfoundland Scale of Happiness–Chinese version. Results showed that participants were happy on average; empty-nest elders were not unhappier than non-empty-nest elders. Elders living without a spouse and rural elders had high risks of being unhappy. Policymakers should thus shift their attention from empty-nest families to the widowed and rural elders.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

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