Aging capital: The potential of sport for developing positive attributes in older adults
Bibliographic record
Abstract
Over the past few decades there has been an impressive increase in the number of older adults participating and competing in sport. Older adult participation in sport is linked to a range of outcomes (both positive and negative), but these outcomes tend to be specific to the older adult population and do not occur automatically. This presentation will summarize our research program examining how sport participation affects indicators of older adult health, ranging from indicators of physical health (e.g., disease and injury), to more global indicators of well-being (e.g., expectations of aging and life satisfaction). Further, we examine the notion that involvement in sport is useful for developing important 'assets' for optimally managing one's aging experience. Borrowing from research in Positive Youth Development, this work assumes that sport is an optimal activity for the development of assets such as confidence and social support. Collectively, this research program emphasizes the potential of sport for improving elements of older adult life, but notes a range of limitations with our current understanding. Importantly, this work indicates adult sport is much more nuanced than previously considered and that future work needs to acknowledge this complexity. Although these data were not exclusively collected in a coached context, they are pertinent to other presentations in this symposium; accordingly, we initiate discussion about how the consequences of older adults' sport participation can be improved via access and availability to quality programming and coaching.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".