Navigating mobile technologies: Older adults’ mobile, digital, and non-digital strategies for enhancing subjective well-being
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it