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Record W4292560520 · doi:10.1177/00332941221123236

Leisure Activity, Leisure Satisfaction, and Hedonic and Eudaimonic Well-Being Among Older Adults With Cancer Experience

2022· article· en· W4292560520 on OpenAlexaff
Sanghee Chun, Sunwoo Lee, Jinmoo Heo, Jungsu Ryu, Kyung Hee Lee

Bibliographic record

VenuePsychological Reports · 2022
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsBrock University
FundersGrantová Agentura České Republiky
KeywordsEudaimoniaPsychologyWell-beingGerontologyLeisure activitySocial psychologyMedicine

Abstract

fetched live from OpenAlex

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.313
Teacher spread0.299 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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