Set in Stone? Mobile Practices Evolution in Later Life
Bibliographic record
Abstract
In what ways do mobile communication practices change through later life? To what extent do sociodemographic characteristics, country of residence, and well-being relate to these changing practices? To answer these questions, we used an online, longitudinal study targeting internet users aged 60 and over in six countries (Austria, Canada, Israel, the Netherlands, Spain, and Romania). The focus is on the 3,125 respondents who declared using a mobile phone in every wave (2016, 2018, and 2020). Results show an increasing usage diversification already before the Covid-19 pandemic. A latent class analysis identified three different styles of mobile practices. The most sophisticated relies on almost all the analyzed functions, while the most unsophisticated is limited to voice calls, texting (mainly SMS), and photographs to a lesser extent. Finally, a multinomial analysis provided a picture of the individual characteristics related to the usage styles in the period. The most relevant dimensions were country of residence and age, followed by internet use intensity. The country of residence is relevant to explaining usage because the telecommunications price structure determines the priority given to the mobile phone in (senior) individuals’ everyday lives. The article contributes nuanced evidence of the trajectories of digital practices in later life. At the same time, the findings support and better inform country-based policies, services, and products for more effective inclusion of the older population in today’s hyper-digitized societies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".