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Record W4352999100 · doi:10.1123/japa.2022-0078

“Older, Faster, Stronger”: The Multiple Benefits of Masters Sport Participation

2023· article· en· W4352999100 on OpenAlexaffabout
Sarah Deck, Alison Doherty, Craig Hall, Angela Schneider, Swarali Patil, Glen R. Belfry

Bibliographic record

VenueJournal of Aging and Physical Activity · 2023
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of AlbertaWestern University
Fundersnot available
KeywordsFeelingPsychologyCognitionGerontologyAthletesPhysical activitySuccessful agingMedicineSocial psychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

While masters sport aligns with the holistic concept of active aging, related research has focused predominantly on the physical domain, and less is known about the psychological, cognitive, and social benefits of older adults' participation. This study examined, in combination, the perceived psychological, social, cognitive, and physical benefits of training and competing as a masters athlete, while considering age and gender differences. Forty masters athletes residing in Canada were interviewed (21 men and 19 women; 15 who were 50-64 years and 25 who were 65-79 years), representing 15 different sports. Interviews were coded both deductively and inductively, revealing several subthemes of benefits for the broader perceived psychological, social, cognitive, and physical benefits, with few but notable differences between women and men, and those younger than 65 years and those 65+ years. Our findings provide new insights into the positive experiences of active aging associated with high levels of physical activity among older adults, such as greater self-confidence, especially for women, comradery, and feeling mentally sharper, especially for the older age group.

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)0.000
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 score0.190
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.047
GPT teacher head0.336
Teacher spread0.289 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

Explore more

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