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Record W3134040641 · doi:10.1123/japa.2020-0270

Adaptation to Athletic Retirement and Perceptions About Aging: A Qualitative Study of Retired Olympic Athletes

2021· article· en· W3134040641 on OpenAlexaff
Michelle Pannor Silver

Bibliographic record

VenueJournal of Aging and Physical Activity · 2021
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsPerceptionAdaptation (eye)AthletesThematic analysisPsychologySuccessful agingGerontologyEmbodied cognitionHealthy agingQualitative researchMedicinePhysical therapySociology

Abstract

fetched live from OpenAlex

Self-perceptions about aging have implications for health and well-being; however, less is known about how these perceptions influence adaptation to major life transitions. The goal of this study was to examine how high-performance athletes' perceptions about aging influenced their adaptation to athletic retirement. In-depth interviews conducted with 24 retired Olympic athletes using thematic analysis yielded three key themes: (a) perceptions about aging influenced participants' postretirement exercise habits, (b) perceptions about aging motivated participants to engage in civic activities, and (c) participants who lacked formative perceptions about aging associated their athletic retirement with their own lost sense of purpose. These findings provide evidence that perceptions about aging influence athletes' adaptation to retirement by directing their subsequent engagement in postretirement activities. Furthermore, this research highlights theoretical implications for the literature regarding embodied processes, retirement transitions, role models, and adaptation to new physical states.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.369

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 head0.088
GPT teacher head0.438
Teacher spread0.350 · 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 designQualitative
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

Citations16
Published2021
Admission routes1
Has abstractyes

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