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Record W2801406795 · doi:10.5014/ajot.2018.031443

Activity Engagement and Everyday Technology Use Among Older Adults in an Urban Area

2018· article· en· W2801406795 on OpenAlexaboutno aff
Ryan Walsh, Ruxandra Drasga, Jenica Lee, Caniece Leggett, Holly Shapnick, Anders Kottorp

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsEveryday lifePsychologyCognitionVariance (accounting)Activities of daily livingGerontologyCognitive impairmentApplied psychologyMedicineBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: We investigated associations among activity engagement (AE), number of available and relevant everyday technologies, ability to use everyday technologies, and cognitive status among older adults in an urban area. METHOD: This cross-sectional study included 110 participants and used three assessments: the Frenchay Activities Index to measure AE, the Everyday Technology Use Questionnaire to measure the number of and ability to use available and relevant everyday technologies, and the Montreal Cognitive Assessment to measure cognitive status. Data analyses used a one-way analysis of variance and a multiple linear regression model. RESULTS: The number of available and relevant everyday technologies was significantly different (p < .001) among groups that varied in level of AE. Ability to use everyday technologies did not significantly differ among groups. Cognitive status did not explain level of AE when the number of available and relevant everyday technologies was considered. CONCLUSION: Increasing the accessibility of available and relevant everyday technologies among older adults in an urban area may increase AE.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.348
Teacher spread0.301 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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