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Record W4411448811 · doi:10.1080/03601277.2025.2521805

Technostress in later life: A multinational study

2025· article· en· W4411448811 on OpenAlexfundaboutno aff
Galit Nimrod

Bibliographic record

VenueEducational Gerontology · 2025
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsTechnostressMultinational corporationPsychologyBusiness

Abstract

fetched live from OpenAlex

To explore sociodemographics and internet use patterns associated with technostress (stress resulting from Information and Communication Technology [ICT] use) among older adults from various countries. An online survey with 3,030 ICT users aged 60 and over from Austria, Canada, Israel, Romania, Spain, and the Netherlands. Mean technostress scores in all participating countries were moderate. Factors most frequently associated with higher technostress levels included poor self-rated health, fewer hours of use, a smaller number of devices used, and less frequent online performance of tasks. In the entire sample, higher technostress correlated with retirement, living with a partner, and residing in Canada, whereas living in Austria or Romania was associated with lower stress. However, the factors most strongly connected to technostress were health and use patterns. The findings reveal more similarities than differences among countries. Improving digital literacy may protect retired and unhealthy older adults from the negative impacts of technology use.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.425
Teacher spread0.395 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractyes

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