Factors Associated With the Ability To Keep Up With Technology Developments: Findings From a National Multigenerational Cross-Sectional Survey in Sweden
Bibliographic record
Abstract
Background: Digital technologies are increasingly central to supporting autonomy, health, and social participation in later life. However, disparities persist in the ability to keep up with technological developments, affecting individuals' opportunities to benefit from digital health and social innovations. Objective: This study aimed to investigate factors associated with individuals' self-reported ability to keep up with technological developments, focusing on generational differences, attitudes toward digital tools, and sociodemographic characteristics. Methods: We conducted a national cross-sectional online survey in Sweden with 2121 respondents aged 30 to 39 years, 50 to 59 years, and 70 to 79 years. Logistic regression analyses were used to identify associations between self-reported ability to keep up with technology and independent variables, including attitudes toward information and communication technology, gender, education, self-rated economic situation, and general health. Results: Most respondents reported being able to keep up with technological developments. Compared to the oldest generation (70-79 years), participants aged 30 to 39 years had 188% higher odds (odds ratio [OR] 2.88, 95% CI 1.84-4.53) of reporting they kept up with technology developments, and women had lower odds than men (OR 0.52, 95% CI 0.39-0.70). Positive attitudes toward information and communication technology being user-friendly (OR 1.81, 95% CI 1.21-2.73), timesaving (OR 2.03, 95% CI 1.44-2.87), and increasing independence (OR 1.99, 95% CI 1.33-2.96) were also significantly associated with keeping up. Conclusions: These findings suggest that digital inclusion in aging societies is shaped by complex and intersecting factors that go beyond age. Promoting equitable digital engagement requires addressing attitudinal, economic, and gender-related barriers and fostering inclusive technology design and support systems for both current and future generations of older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".