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Record W2113159286 · doi:10.1017/s0144686x11001140

How do Older Masters Athletes Account for their Performance Preservation? A Qualitative Analysis

2012· article· en· W2113159286 on OpenAlexaff
Rylee A. Dionigi, Sean Horton, Joseph Baker

Bibliographic record

VenueAgeing and Society · 2012
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsYork UniversityUniversity of Windsor
Fundersnot available
KeywordsCompetitor analysisAthletesCompensation (psychology)Perspective (graphical)Competition (biology)PsychologyApplied psychologySocial psychologyMarketingBusinessMedicineComputer sciencePhysical therapy

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of this study was to examine how older people make sense of their capacity to maintain sports performance. Performance maintenance is predominantly examined from a quantitative perspective, with little attention given to how people themselves account for it. We interviewed 44 competitors (23 females, 21 males) from the 2009 Sydney World Masters Games (aged 56–90 years; mean = 72 years). The major themes were: ‘Use it or lose it’ (performance preservation required specific ‘training’ and the continuation of general physical activity); ‘Adapt’/‘modify’ (participants compensated for their decline in speed, strength and endurance so they could continue competing in sport); ‘It's in my genes’ (participants attributed their ‘family history’ and/or innate ‘determination’ to performance maintenance); and ‘I like to push myself’ (participants valued improved performance, pushing their bodies and winning which motivated them to continually train and compete). The findings are discussed within a framework of three key performance maintenance theories: (a) preserved differentiation, (b) selective maintenance and (c) compensation. Although compensation and continued training are effective ways to counter decline in later life, this study extends past research by showing how older athletes tend to combine and/or generalise stable and unstable attributes of performance preservation. In particular, this research highlights the importance individuals and Western society place on self-responsibility for health, competition and performance maintenance, which act as key motivating factors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.456

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 head0.050
GPT teacher head0.362
Teacher spread0.312 · 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

Citations30
Published2012
Admission routes1
Has abstractyes

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