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Record W3162177730 · doi:10.1145/3411764.3445702

Technology Adoption and Learning Preferences for Older Adults:

2021· article· en· W3162177730 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsComputer scienceHuman–computer interactionKnowledge management

Abstract

fetched live from OpenAlex

Technology adoption among older adults has increased significantly in recent years. Yet, as new technologies proliferate and the demographics of aging shift, continued attention to older adults’ adoption priorities and learning preferences is required. Through semi-structured interviews, we examine the factors adults 65+ prioritize in choosing new technologies, the challenges they encounter in learning to use them, and the human and material resources they employ to support these efforts. Using a video prototype as a design probe, we present scenarios to explore older adults’ perceptions of adoption and learning new technologies within the lens of health management support, a relevant and beneficial context for older adults. Our results reveal that participants appreciated self-paced learning, remote support, and flexible learning methods, and were less reliant on instruction manuals than in the past. This work provides insight into older adults’ evolving challenges, learning needs, and design opportunities for next generation learning support.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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

Quick stats

Citations116
Published2021
Admission routes1
Has abstractyes

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