Mobile phone use before and during the COVID-19 pandemic – a panel study of older adults in seven countries
Bibliographic record
Abstract
The aim of this study was to investigate the changes in older adults’ mobile phone use from before to during the COVID-19 pandemic. The media displacement and digital divide approaches served as the theoretical frameworks of the study. The data were drawn from the 2018 and 2020 waves of the Aging + Communication + Technology cross-national longitudinal panel study. The sample consisted of older Internet users, aged 62 to 96 (in 2018), from Austria, Canada, Finland, Israel, the Netherlands, Romania, and Spain, who participated in both waves (N = 4,398). Latent class analysis and latent transition analysis with multinomial regression models were the main methods applied to the data. With regard to the findings, three mobile phone function use profiles—Narrow Use, Medium Use, and Broad Use—were identified from the data. Lower age, being married, higher income, and place of residence (in 2018) predicted belonging to the three profiles, while country differences in the prevalence of the profiles were substantial. Between 2018 and 2020, transition from one profile to another was relatively rare but typically toward the “Broad Use” category. Profile transitions were most common in Romania, while stability was highest in Finland, Israel, and Canada. In addition, gender, age, marital status, and place of residence predicted the likelihood of changing from one profile to another between 2018 and 2020. The results suggest that older adults’ mobile phone function use is relatively stable over a two-year time span. While new mobile phone functions are adopted, they seem to augment the spectrum of mobile usage rather than displace older similar functionalities. In addition, demographic, socioeconomic, and country-level digital divides, although slightly modified over time, remain significant among older adults.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".