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Record W2586302308 · doi:10.1080/21931674.2016.1277861

Older people and the use of ICTs to communicate with children and grandchildren

2017· article· en· W2586302308 on OpenAlexaboutno aff
Loredana Ivan, Mireia Fernández-Ardèvol

Bibliographic record

VenueTransnational Social Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsAppropriationGratificationThe InternetIncentivePsychologySet (abstract data type)SociologyPublic relationsAdvertisingSocial psychologyInternet privacyBusinessPolitical scienceComputer scienceWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.321
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2017
Admission routes1
Has abstractyes

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