Sampling and athlete development in the youth sport context: A systematic review
Bibliographic record
Abstract
Sport specialization has been linked to a variety of negative outcomes in young athletes including an increased risk of injury, burnout, and attrition. In an effort to combat these issues, researchers and health practitioners recommend young athletes avoid year-round, intensive participation in a single sport and instead sample a breadth of sport programs at varying intensities. Due to the recent spotlight that sampling has enjoyed in both the academic and public realm, the purpose of this review was to systematically investigate the youth literature and synthesize findings involving outcomes of interest in relation to sampling in sport—namely: improved sport performance, increased likelihood of sport participation, and enhanced personal development (the 3Ps; CA´tA© et al., 2014). Six electronic databases were searched yielding 9,257 articles. Captured articles were read at the abstract level and retained for analysis if they described the impact of sampling on any of the 3Ps. In total, 42 articles met the inclusion criteria and were coded for outcomes of sampling. Findings indicated that youth sampling research has: (a) primarily used quantitative approaches, (b) almost exclusively implemented retrospective methodologies of inquiry, (c) predominantly included male participants, and (d) prioritized findings related to athlete performance, rather than sport participation and personal development. It is hoped that these findings might guide researchers interested in sampling to explore more diverse methodologies and to include underrepresented athlete populations in future studies. Exploring these avenues could prove important in painting a more complete picture of the contemporary young athlete experience.
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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 | 0.025 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".