Older Adults’ Perceptions of ICT: Main Findings from the Technology In Later Life (TILL) Study
Bibliographic record
Abstract
Technology is entwined in 21st Century society, and within the lives of people across all ages. The Technology In Later Life (TILL) study is the first piece of work contributing to the impact, behavior, and perception of technology use, by adults aged ≥70 years, residing in rural and suburban areas. TILL is an international, multi-centred, multi-methods study investigating and conceptualizing how various technologies impact the lives of older adults; residing in urban and rural locations in the United Kingdom (UK) and Canada. This in-depth study recruited 37 participants via a multi-methods approach. Analysis of the findings ascertained two overarching themes: facilitators of technology use (i.e., sharing of information and feeling secure), and detractors of technology (i.e., feelings of apprehension of use). Proposed recommendations include promotion of technology from a strengths-based perspective focusing on positive opportunities technology to improve health and wellbeing, creating a peer support network to assist with learning of new technology, and the need to examine further how intergenerational relationships may be enhanced through the use of technology. The distinction of these themes narrates to the originality of this initial study and milieu of recruited participants, intersecting across the fields of gerontology, geography, social sciences, and gerontechnology.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".