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Record W3169982293 · doi:10.26355/eurrev_202105_25938

Attitude and perceptions of older and younger adults towards ambient technology for assisted living.

2021· article· en· W3169982293 on OpenAlexaff
Mohamed-Amine Choukou, Y Sakamoto, Priti Irani

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransformative learningPerceptionIndependence (probability theory)GerontologyHealth carePsychologyMedicineDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: Healthcare systems are challenged by the rapidly increasing number of older adults requiring services to maintain at-home independence. Technology, such as ambient sensing, has been identified as one potential solution to address these issues. This study's aim is twofold: (1) to explore the general perception of older and younger adults about the transformative role technology can play in their health care as they age, and (2) the generation of health solutions in home care. SUBJECTS AND METHODS: This study explores data collected from an online survey involving 367 participants from North America and South Asia. RESULTS: Our analyses yielded that the older adult participants had a generally positive attitude toward employing technologies and that younger adults were less concerned about the use of ambient sensing. Notably, however, they all reported relatively deep concerns about the potential use of homecare service technologies. Our results showed heterogeneity of technology literacy among older adults. CONCLUSIONS: Both older and younger adults perceive ambient technology for assisted living as a promising solution to enable older adults' at-home independence. Regardless of age, potential users of these technologies showed concerns with possible breaches of individual privacy, personal data, and personal health information.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.379

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 studies0.0000.001
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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