Leisure Activity, Leisure Satisfaction, and Hedonic and Eudaimonic Well-Being Among Older Adults With Cancer Experience
Bibliographic record
Abstract
Older adults with cancer experience are more likely to encounter a notable reduction of participation in physical and social leisure activities, which may threaten their overall well-being. The purpose of this study was to explore how specific types of leisure activities and leisure satisfaction were linked to hedonic and eudaimonic well-being among older adults who had experienced cancer. A nationally representative sample of 2,934 older adults with lifetime cancer experience was retained from the Health and Retirement Study. The results of regression analysis revealed that walking for 20 minutes was reported as the only type of leisure activity related to hedonic well-being for the oldest-old (85+ years old). The current study also found that TV watching was significantly, but negatively associated with eudaimonic well-being for the young-old (50-74 years of age). In contrast, using a computer was positively linked to hedonic and eudaimonic well-being among the young-old and old-old (75-84 years of age). The current study made a significant contribution to build the body of knowledge that the different age groups of older adults who had experienced cancer can enhance eudaimonic and hedonic well-being by participating in different types of leisure activities. Implications for further research are discussed.
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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 | 0.000 | 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 | 0.000 | 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".