Sport participation profiles of high-performance adolescent athletes: Initial and secondary sport sampling
Bibliographic record
Abstract
Some models of sport participation suggest that children begin by sampling several sports before specializing in late adolescence (e.g., CA´tA©, 1999). However, the pathways to talent development may not be captured by linear models depicting normative stages of progression. The purpose of this study was to examine the sport participation profiles of high-performance adolescent athletes. Data were generated through in-depth, semi-structured interviews with ten athletes (8 female, 2 male, Mage = 16.1 years, SD = 0.58) who competed at a provincial level or higher, and the parent most involved in their sport participation (8 mothers, 2 fathers, Mage = 48.0 years, SD = 5.16). Participants also completed sport participation timelines. Inductive analysis showed that participants' sport participation was marked by two phases of sampling. Initial sampling (typically ages 3-9) was characterized by playing several sports sequentially for short durations rather than playing multiple sports simultaneously. There appeared to be a focus on developing physical literacy during this period. Initial sampling was followed by gradually specializing into a main sport (typically ages 8-10). While continuing to participate in their main sport, secondary sampling (typically ages 10-14) often occurred through exposure to new sports through school sport. Results also indicated that initial sport sampling was primarily parent-driven, whereas secondary sampling was athlete-driven and also often influenced by peers. In conclusion, this study suggests that sport sampling is not a singular phase of talent development, but rather a process that appears to have two phases that coincide with gradual sport specialization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".