MétaCan
Menu
Back to cohort
Record W2770707498 · doi:10.1057/978-1-137-48562-5_17

Sport, Physical Activity, and Aging: Are We on the Right Track?

2017· book-chapter· en· W2770707498 on OpenAlexafffund
Kelly Carr, Kristy L. Smith, Patricia L. Weir, Sean Horton

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2017
Typebook-chapter
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsPsychologyGerontologyPopulation ageingHealthy agingPopulationMedicine

Abstract

fetched live from OpenAlex

This chapter provides a critical overview of the applicability and effectiveness of ‘Sport for Life’ and ‘Sport for All’ approaches in achieving ‘success’ during older adulthood. Older adulthood, as suggested by the World Health Organization (WHO), commonly coincides with the eligibility to collect pension payments, and thus begins between 60 and 65 years of age (WHO, 2002a). As many parts of the world face a demographic shift toward this older population base, a critical examination of strategies to maintain health, function, and well-being into later life is warranted. Positioned within a framework of successful aging, this chapter discusses the implications of holding the individual accountable for personal health and functioning, while aligning this notion with the expectation of all individuals to maintain physically active lives through sport participation. As an example of older adults participating in sport we highlight the World Masters Games and debate the use of competitive Masters athletes to exemplify the aging ideal as well as serve as role models for the senior population. We consider barriers to sport and physical activity participation and provide a snapshot of engagement profiles throughout older adulthood. Conclusions are drawn regarding ‘Sport for Life’ and ‘Sport for All’ approaches when encouraging older adults to age successfully, and contrasts are made to the broader framework of ‘active aging’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.006

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 head0.065
GPT teacher head0.343
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooksSame topicAging and Gerontology ResearchFrench-language works237,207