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Record W1972722770 · doi:10.1080/09638280802572940

Can personal and environmental factors explain participation of older adults?

2009· article· en· W1972722770 on OpenAlexaff
Dana Anaby, William C. Miller, Janice J. Eng, Tal Jarus, Luc Noreau, PACC Research Group

Bibliographic record

VenueDisability and Rehabilitation · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité LavalGF Strong Rehabilitation CentreUniversity of British Columbia
Fundersnot available
KeywordsPsychologySocial supportBalance (ability)Activities of daily livingSocial engagementGerontologyDepression (economics)Interpersonal communicationScale (ratio)MedicineSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: This study explores the extent to which personal and environmental factors explain participation in daily activities and social roles of older adults with chronic conditions. METHOD: Two hundred older adults with chronic conditions completed the following assessments: Assessment of Life Habits (participation); Interpersonal Support Evaluation List (social support); Activities Specific Balance Confidence Scale (balance confidence); Timed Up and Go Test (mobility capacity); and Centre for Epidemiological Studies Depression Scale (depression symptomatology). RESULTS: Mobility and balance confidence explained 30% of the level of participation in daily activities and 24% of participation in social roles, whereas social support and depression did not contribute to the explanation of participation. When explaining participation in daily activities, sex had a significant contribution to the model. CONCLUSIONS: Participation accomplishment is explained by personal factors related to an elder's physical and mental ability while sex differences had an important role for explaining accomplishment of daily activities. Additional aspects of participation, environmental barriers, and level of disability, are key factors identified for further inquiry.

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 score0.027
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.411
Teacher spread0.375 · 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

Citations58
Published2009
Admission routes1
Has abstractyes

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