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Record W2125621563 · doi:10.1017/s0714980800002075

Medical and Everyday Assistive Device Use among Older Adults with Arthritis

2002· article· en· W2125621563 on OpenAlexaff
Deborah Sutton, Monique A. M. Gignac, Cheryl Cott

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2002
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsEveryday lifeAdaptation (eye)Assistive deviceActivities of daily livingGerontologyMedicinePsychologyPhysical therapyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

ABSTRACT This research compared older adults' use of medical assistive devices (ADs) with their use of everyday ADs as a means of managing chronic physical disability. The study also examined whether predisposing, need, and enabling factors were associated with device use in three domains of activity: personal care / in-home mobility, household activities, and community mobility. Participants were 248 adults, aged 55 years and older, with a wide range of disability levels as a result of osteoarthritis. All participants were administered an in-depth, structured questionnaire, as part of a larger study examining older adults' independence and adaptation to chronic physical illness. The results revealed that respondents actively adapted to their disabilities and used a wide range of medical and everyday devices, with everyday devices being reported more than twice as often as medical ADs and the fewest devices overall being reported for community mobility. In general, medical devices were used when subjective and objective need for ADs was considerable. Everyday devices were reported earlier in the trajectory of the disease, at mild and moderate disability levels, and were associated with a broader pattern of adaptation that included planning to avoid problems, exercise, and pacing activities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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.

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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicAssistive Technology in Communication and MobilityFrench-language works237,207