An exploration of physical literacy in Masters Athletes
Bibliographic record
Abstract
Sport participation among older adults, is increasing, but many still fall short of meeting physical activity (PA) guidelines. Master athletes, who engage in PA through systematic training, and competition, offer unique insights into PA practices in later life. This study explored whether master athletes embody the principles of physical literacy and how their experiences could inform strategies to promote PA among older adults. An electronic survey completed by 35 master athletes (55–75 years, 20 female) and follow-up interviews with eight participants revealed that most were unfamiliar with the term "physical literacy," yet they intuitively practiced its principles. Master athletes identified parallels between their behaviors and physical literacy but viewed existing models as primarily youth-focused and less applicable to older adults. Additionally, lifelong sport participation was not universal; nine participants, predominantly women, began competitive sports later in life. A key finding was the importance of social connectedness, which emerged as a critical motivator and enabler for sustained PA among master athletes. This element, largely absent from current physical literacy frameworks, may be vital for engaging older adults in PA. Integrating social connection into physical literacy models could address barriers unique to this demographic, enhancing participation and promoting healthier aging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".