Older Athletes’ Perceived Benefits of Competition
Bibliographic record
Abstract
Intense sport competition is typically associated with young people. Also, much of the literature on exercise for older adults focuses on benefits derived from regular physical activity, such as walking, dancing and fitness classes, and suggests that one should avoid extremely strenuous exercise. The rising phenomenon of older people competing in sport presents a challenge to these assumptions. In 2009, approximately 28,000 athletes from 95 countries gathered in Sydney, Australia to compete across 28 different sports at the World Masters Games. We interviewed 44 competitors (23 females, 21 males; aged 56-90 years; M=72) about what they gained from competing in sport that extended beyond non-competitive physical activity outcomes. Five key themes emerged from the data. The first theme, “I like a challenge”, depicts Masters sport as an ideal context to test one’s abilities. In particular, lifelong athletes (or those who had returned to sport after a long break) enjoyed the satisfaction of knowing they “can still do it”! On the other hand, Masters competitions provide space for older people to begin sport in later life, as the second theme highlights, “I discovered that at this age group I could win things”! Theme three, “I’m more motivated to work harder”, describes how regular competitions provided goals for participants which structured their training. Also, the act of competing brought out their best performances. The fourth theme, “You know where you stand”, shows how participants liked that competition enabled them to compare themselves with others of their own age cohort. The final theme, “Travel” and “companionship”, explains how the organized, competitive structure of Masters sport and its club system allowed for regular travel, the establishment of ongoing friendships and weekly social interaction. Our data suggest that sport provides unique benefits to participants above and beyond those gained from general physical activity.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".