Sport, Physical Activity, and Aging: Are We on the Right Track?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".