Media-Based Leisure and Wellbeing: A Study of Older Internet Users
Bibliographic record
Abstract
Studies exploring digital technology in the context of leisure for older people tend to neglect their parallel use of traditional media. By simultaneous examination of both online and offline recreational media use, the present study explores media-based leisure repertoires and wellbeing among older Internet users. Data were collected via a survey of 10,527 Internet users aged 60 and up from seven countries (Austria, Canada, Denmark, Israel, the Netherlands, Romania, Spain). Analysis examined participants’ media use and differences among people with disparate use patterns. The study identified four groups of Internet users according to the media-based leisure activities they engaged in: innovative traditionalists, entertainment seekers, selective content consumers, and eclectic media users. The groups differed in their activity repertoires, background characteristics, and leisure preferences. Being an eclectic media user (i.e., relatively less selective) was significantly associated with lower life satisfaction. Results indicate an advantage to selectivity in media use for leisure and confirm that participation in certain activities may compensate somewhat for distressing conditions in old age. They also suggest diminished boundaries between offline and online leisure among older Internet users and call for further development of the functional approach to Internet use in later life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".