Aging Well in the Digital Age: Technology in Processes of Selective Optimization with Compensation
Bibliographic record
Abstract
OBJECTIVES: Studies show that using information and communication technology (ICT) contributes significantly to elders' subjective well-being (SWB). Drawing on the Selective Optimization with Compensation (SOC) model, this study aims at exploring the mechanism by which ICT use helps older adults remain engaged in valued life activities and maintain their SWB. METHOD: Involving teams from seven countries (Canada, Colombia, Israel, Italy, Peru, Romania, Spain), 27 focus groups were conducted with a total of 184 grandmothers aged 65 years and older who use ICT. RESULTS: Analysis led to identification of a series of strategies related to ICT use that may be described in SOC terms. "Intentional limited use" and "Selective timing,", for example, are clearly associated with selection. In addition, numerous optimizing strategies were found to be applied in "Instrumental" and "Leisure" activities, whereas some ICT uses offered compensation for "Aging-related" and "General" challenging circumstances. DISCUSSION: The study suggests that ICT is used in all three SOC processes and that its effective application facilitates adjustment and enhances SWB. It should therefore be regarded as a resource that supports existing personal and social resources and life management strategies, and even as a Quality of Life Technology that maintains or enhances functioning in older adulthood.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".