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Record W1514895048 · doi:10.1300/j016v25n03_02

Valued Activities of Everyday Life Among the Very Old

2001· article· en· W1514895048 on OpenAlexaffabout
Richard Lefrançois, Gilbert Leclerc, Micheline Dubé, Suzanne Hamel, Philippe Gaulin

Bibliographic record

VenueActivities Adaptation & Aging · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité du Québec à Trois-RivièresHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsActivities of daily livingPsychologyLongitudinal studyPhysical activitySocial activityGerontologyEveryday lifeLongitudinal dataDevelopmental psychologyDemographyMedicineSocial psychologySociologyPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

Based on panel data from the Quebec Longitudinal Study on Aging, this article investigates the preferred types of activity of 80-85 year-olds, contrasting for gender and functional health condition. Both MANCOVA (p <.05) and ANCOVA (p <.01), with repeated measures, were used to assess the significance of a change in activity commitment. Results showed that respondents were more involved in emotional, spiritual, and social types of activity. Engagement in activities among older adults did not change significantly over a 1-year period. A reduced capacity in performing instrumental activities of daily living was found to have the most detrimental effect on valued activity. These results indicate that incapacity may have an adverse impact not only on physical activity, but on all other types of activity as well. The analyses suggest that the elderly manage to compensate for the loss in one type of activity by increasing their commitment in other types.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2001
Admission routes2
Has abstractyes

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