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Interests among Older People in Relation to Gender, Function and Health-Related Quality of Life

2013· article· en· W2075962748 on OpenAlexaboutno aff
Anette Källdalen, Jan Marcusson, Ewa Wressle

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersFamiljen Janne Elgqvists Stiftelse
KeywordsRecreationQuality of life (healthcare)GerontologyGeriatric Depression ScaleDepression (economics)CognitionMental healthActive ageingMedicinePsychologyDepressive symptomsOlder peoplePsychiatryNursing

Abstract

fetched live from OpenAlex

Introduction: Older people should have opportunities to be active participants in society because aspects such as lifestyle, physical and social environment and physical and mental status have influence on active ageing. The purpose of this study was to explore the interests pursued by 85-year-old people living in ordinary housing in relation to gender, cognition, depression and health-related quality of life (HRQoL). Method: A sample of 240 participants completed a postal questionnaire, including the EuroQoL HRQoL measurement. Additional instruments used during a subsequent home visit were the Canadian Occupational Performance Measure, Mini Mental State Examination and Geriatric Depression Scale. Results: Women experienced poorer health than men, lived alone to a greater extent and used more mobility devices. Compared with men, women had a larger number of interests within household management, but there were no gender differences in the leisure area. A lower number of interests in active recreation was associated with lower cognitive function, poorer HRQoL and a higher risk of depressive symptoms. Conclusion: The main finding is that engaging in active recreation interests is associated with better cognition, less depression and higher HRQoL in these 85-year-old people and is, therefore, a concern of occupational therapists.

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)0.002
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 score0.032
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.140
GPT teacher head0.410
Teacher spread0.270 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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