Navigating mobile technologies: Older adults’ mobile, digital, and non-digital strategies for enhancing subjective well-being
Bibliographic record
Abstract
Older age cohorts have been found to exhibit both less interest and less use of digital technologies than younger cohorts, which suggests that they may be less flexible in comparison to younger technology users. However, frequency is not the only differentiating factor between age groups in the context of mobile technology use, as the specific ways in which technologies are used also play a significant role in the daily lives of older adults (65+). Drawing on the selective optimization with compensation (SOC) model, we ask what strategies older adults use to enhance their subjective well-being when using mobile technologies. The thematic analysis is based on 20 elicitation interviews conducted in Central Finland in 2018. Our findings suggest that mobile technologies can act as both a tool to enhance well-being and a source of problems for older adults, and that older adults show considerable creativity in navigating various mobile, digital and non-digital strategies. Furthermore, we argue that these evolving, and thus also in this sense mobile, strategies contribute to the subjective well-being and successful ageing of older adults by providing them with “workarounds” to manage mobile technologies to their benefit.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".