Does digital leisure relate to subjective well-being in later life? Examining roles of enjoyment, social support, and capitalisation
Bibliographic record
Abstract
With a rapid increase in use of information and communication technology (ICT) among older adults, benefits of ICT use become an important concern for research. ICT use is an opportunity to experience leisure in a new way for many older adults, also identified as digital leisure. Although digital leisure may facilitate subjective well-being (SWB), it is not clear in what specific mechanisms digital leisure relates positively to SWB in later life. To address this gap in the literature, we examined whether participation time, enjoyment, social support, and capitalisation (i.e. sharing positive life events with others) during digital leisure had positive correlations with SWB, measured by happiness and life satisfaction, among older adults. Data were collected from 351 participants aged 65 years old or older. Multiple regression analysis was conducted to analyse these data. Our results demonstrated that enjoyment, social support, and capitalisation during digital leisure correlated positively with SWB, whereas participation time did not. In addition, there was a significant interaction between social support and capitalisation. We discuss implications of these results in terms of fostering older adults’ SWB by facilitating their experiences of enjoyment, social support, and capitalisation through digital leisure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".