Older people and the use of ICTs to communicate with children and grandchildren
Bibliographic record
Abstract
In this research we explore older people’s incentives to use Internet services to communicate with their children and grandchildren, and the factors that make older individuals stop using (or even reject) Internet-mediated communications. We apply the uses and gratifications theory, and the gratification niche of medium concept to understand the way people return to less sophisticated tools of communication once the marginal utility is lost. Our analysis is based on empirical evidence the two authors gathered in a set of case studies. We conducted semi-structured interviews with people aged 60 and over in Barcelona, Romania (Bucharest and rural areas), Toronto, Los Angeles, Montevideo, and Lima. The results show that communicating with children and grandchildren when families get separated is an important motivator that “pushes” the elderly to learn more about the use of information and communication technologies (ICTs). We emphasize the fact that once motivation is lost (i.e. when family members are back home) the interest in using a particular technology to communicate is diminished, therefore older people might stop using it. We argue for a more dynamic model of technology appropriation for this age group that includes successive stages: ignoring, appropriation, rejection, and re-appropriation.
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".