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Record W4213240365 · doi:10.3390/ijerph19042235

Experiential Value of Technologies: A Qualitative Study with Older Adults

2022· article· en· W4213240365 on OpenAlexaff
S. Desai, Colleen McGrath, Heather McNeil, Heidi Sveistrup, Josephine McMurray, Arlene Astell

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsToronto Rehabilitation InstituteWilfrid Laurier UniversityUniversity of OttawaWestern UniversityUniversity of TorontoYork University
Fundersnot available
KeywordsExperiential learningAging in placeQualitative researchEmerging technologiesCurrencyValue (mathematics)PsychologyPopulationInternet privacyKnowledge managementPublic relationsMarketingMedical educationBusinessGerontologyMedicineSociologyComputer sciencePedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This study investigated the experiences of older adults with technologies they own and determined how they value them. Thirty-seven older adults participated in a Show and Tell co-creation session at a one-day workshop. Participants described why they loved or abandoned technologies they own. Their responses were recorded and analysed using Atlas.ti 22.0.0. Seven main themes representing experiential value in older adults emerged from the analysis: Convenience, Economy, Learning and Support, Currency of Technology, Privacy and Security, Emotions and Identity aspects of their experiences. This qualitative study has resulted in implications to design that recommends (a) Design for product ecosystems with technologies and services well-coordinated and synchronized to facilitate use of the technology (b) Create awareness and information on privacy and security issues and technical language associated with it (c) Make anti-virus and anti-phishing software accessible to older population (d) Design technologies as tools that allow older adults to identify themselves in the community and family (e) Create services that make technologies and services in the ecosystem affordable for the older adults. The outcomes of this study are significant as they provide recommendations that target systemic issues which present barriers in the use of technology.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.434
Teacher spread0.382 · 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 designQualitative
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

Citations33
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Environmental Research and Public HealthSame topicTechnology Use by Older AdultsFrench-language works237,207