Beyond WhatsApp: Older people and smartphones
Bibliographic record
Abstract
<p>This paper analyzes how older people, living in Spain, use smartphones and smartphone applications. Using a mixed methods approach, we compare quantitative results obtained by tracking mobile app usage amongst different generational samples with qualitative, focus-group discussions with active smartphone users. A sample of Spanish smartphone users were tracked during one month in the winter of 2014 (238 individuals, aged 20 to 76 years-old). This was followed by three focus group sessions conducted in the spring of 2015, with 24 individuals aged 55 to 81. As we learned, WhatsApp is currently the most popular application used by people of all ages, including older adults. Smartphones increasingly are playing a central role in the life of older participants, although the frequency of app access is negatively correlated with age. On the other hand, as our data indicates, older adults also use a number of different types of apps that are distinct from that of younger users. Older participants access personal information manager apps (calendar, address book and notes) more often than other age groups. And comparatively, older participants use the smartphone less often in stable locations (home, office, relatives’ home) with Wifi than somewhere else and with mobile data. As we argue, differences in age seem to reflect the evolution in personal interests and communication patterns that change as we grow older. Our study captures new trends in smartphone usage amongst this cohort. It also indicates how a combination of methods may help to assess the validity of the log and qualitative data. We highlight the relevance of conducting careful generational studies in smartphone use and some of the potentials and limitations of making predictive studies of ICT use as we change throughout the life course. Finally, we assert the value of the inclusion of older representatives within research, which ultimately may influence public decisions and the design of new technologies.</p>
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".