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Record W2127221146 · doi:10.1093/geront/42.2.217

Continuing and Ceasing Leisure Activities in Later Life: A Longitudinal Study

2002· article· en· W2127221146 on OpenAlexaffabout
Laurel A. Strain, Carmen Grabusic, Mark S. Searle, Nicole Dunn

Bibliographic record

VenueThe Gerontologist · 2002
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of WinnipegUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsLongitudinal studyLeisure activityMarital statusPsychologyGerontologyPeriod (music)Leisure timeActivities of daily livingReading (process)Developmental psychologyPhysical activityDemographySocial psychologyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: This study examined changes in leisure activities of older adults over an 8-year period, and associated sociodemographic and health characteristics. DESIGN AND METHODS: Data were from a longitudinal study conducted in Winnipeg, Manitoba, Canada; 380 respondents were interviewed in-person in both 1985 and 1993. Changes in ten specific activities and the overall number of activities continued were examined. RESULTS: Theater/movies/spectator sports and travel were the activities least likely to be continued over the 8-year period; watching television and reading were most likely to be continued. Characteristics significantly related to changes in activities were age, gender, education, and self-rated health in 1985 as well as changes in marital status, self-rated health, and functional ability between 1985 and 1993, although no consistent pattern emerged. IMPLICATIONS: Leisure education is discussed as a means of introducing modifications to enhance older adults' participation in desired activities. Directions for future research are highlighted.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.087
GPT teacher head0.332
Teacher spread0.245 · 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

Citations220
Published2002
Admission routes2
Has abstractyes

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