Challenges and Solutions to Supporting Physical Literacy within Youth Sport
Bibliographic record
Abstract
There are current global concerns surrounding the lifestyle behaviours and future health and well-being of youth. One concept that has gained traction to address these concerns is physical literacy (PL). Organised youth sport is one context that can promote PL, offering multiple benefits coupled with a range of challenges. This Leading Article aims to provide a balanced overview of the key challenges associated with supporting PL within youth sport and offers solutions to overcome these challenges. The first challenge focuses upon attracting youth (and parents) to sport through increasing recruitment against social constraints (e.g., socioeconomic), popular entertainment (e.g., streaming) and family issues (e.g., scheduling). The second centres on retaining children in sport to maximise participation through the appropriate design, organisation and delivery of training and competition opportunities. The final challenge relates to the talent pathway and how sports can structure (e.g., [de]selection) and deliver (e.g., training intensification) a pathway to ensure that all youth athletes flourish along their PL journey. Our solutions focus on organisations (e.g., national governing bodies, clubs) understanding and considering, (1) PL as an individual's relationship with movement and physical activity throughout life, (2) children's rights (e.g., interests, opportunities, expression of views), and (3) sport policies and practices when designing and delivering sport experiences. Whilst these challenges and solutions are wide ranging and complex, our belief is that the adoption of a PL approach by stakeholders when designing, delivering and enacting sport programs can enhance the experiences of youth involved in sport and ultimately support their lifelong PL journey.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".