Are Empty-Nest Elders Unhappy? Re-examining Chinese Empty-Nest Elders’ Subjective Well-Being Considering Social Changes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".