Successful aging: a cross-national study of subjective well-being later in life
Bibliographic record
Abstract
This paper aims to identify and analyze the life course and contextual factors that influence the subjective well-being (SWB) of individuals over 60 years of age. Our research is based on the results of the 5th wave of the World Value Survey. We have investigated the level of SWB for older people at both the individual and country level. The results of our research demonstrate that the strongest predictors of SWB later in life are satisfaction with one’s financial state, health, and a sense of control, meaning the belief that individuals are in control of their lives. Besides this, the important factors of SWB for older people are the ability to establish and maintain friendly relations with other people, such as family members and friends, and to invest their own resources in positive emotions and important relationships for themselves. Older people from ex-communist countries have the lowest level of SWB. Older people from English-speaking countries, such as the United States, Canada, New Zealand, and the United Kingdom, have, by contrast, the highest level of SWB. These results suggest that the degree of modernization influences SWB levels very strongly. For older people, the country in which they live, the level of democracy, GDP per capita, freedom, and tolerance are very important. In contemporary society, the later period of life is a time for self-realization, new activities, new leisure, and new emotions. If society understands the needs of older people and provides opportunities for their realization, society can overcome the challenges caused by population aging. Only then can we discuss the concept of ‘successful aging’
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".