Determination of Subjective Well-being among Empty-nest Elderly and Its Related Factors in Guangdong Province, China
Bibliographic record
Abstract
With the arrival of an aging society and the acceleration of globalization, industrialization, and urbanization, there are more and more empty nest elderly. It is expected that by 2030, the elderly population in China will be nearly 300 million, and the proportion of empty nest elderly households will reach 90%. The living conditions of empty nest elderly people are not only related to their physical and mental health, but also to the stability of the country and social development. The aim of this study is to explore the status of happiness and social support of empty nesters in Guangdong Province and analyze the relationship between the above two variables. Totally 1148 empty nesters (776 males, 734 females) from 5 cities in Guangdong province are selected by stratified random sampling and conducted with Memorial University of Newfoundland Scale of Happiness (MUNSH), Social Support RatingScale (SSRS), Mini-Mental State Examination (MMSE) and a self-edited questionnaire on the general information. The survey results indicate the following 3 points. First, The total score of MUNSH is (10.20±6.37). Second, the total score and the scores of the 3 dimensions of objective support, subject support, the use of support in SSRS are (30.79±5.51), (9.24±2.37), (19.38±4.95) and (9.22±2.15) respectively. Third, multiple variable linear regression show that the following 9 factors (\(\beta\)=.217, .316, .152, .587, .513, .146, .166, .114, .198 respectively, all P< .05) are positively associated with the total score of MUNSH, like living mode, marital status, educational level, health status, pensions level, frequency of children visiting, whether like to play old-type poker or mahjong, whether keep pets, and social support total score. Some items are negatively correlated with MUNSH scores (\(\beta\)=-.413, -.521, -.130, -.077, all P< .05), such as self-rated loneliness degree, whether worry about medical expenses, housing problems, whether need to get the day-care orlegal aid services. It is therefore suggested that the physical health, personal economic condition, leisure interest, children’ care, and social support may be related factors of subjective well-being among empty-nest elderly.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".