Exploring resiliency in young and old athletes
Bibliographic record
Abstract
Sport participation has been advocated as an avenue to develop or enhance various positive developmental outcomes. One of these, resiliency, has emerged an important asset for dealing with adversity. For instance, previous research in youth sport has found resiliency to play a role in managing stressors such as burnout, anxiety and depression. Similarly, literature in older adults has explored resiliency as an important factor in managing loneliness, depression and overall health. However, unlike youth research, little is known about this construct within the context of sport for older adults. Given the scarcity of this topic, this study used the General Social Survey 2016 (cycle 30) to compare resiliency among older athletes, older non-athletes (aged 45 and above) and younger athletes (aged 15-34). Preliminary results indicated that both older (M=40.62, SD=3.67) and younger athletes (M=41.76, SD=3.87) had significantly higher resiliency than older non-athletes (M=37.81, SD=4.87). However, there was no significant difference between young and older athletes. While these results are intriguing, future work on the contribution of sport to levels of resilience among athletes over the lifespan is needed to determine whether this is a cause or effect of sport participation in older life. Additionally, comparisons to other forms of leisure activities are needed to determine if sport or any form of active leisure is related to increases in resilience. Moreover, further exploration in this area could have implications for regulating psychological stressors related to well-being in older adults.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".