Bibliographic record
Abstract
An athlete's family is known to play a significant role in their development and, arguably, their level of sport attainment (Knight, 2017). The current investigation sought to explore how family members' physical activity and sport involvement related to athletes' skill level. Data collected using the Developmental History of Athletes Questionnaire (DHAQ; Hopwood, 2013) on 229 athletes (M = 24.6 years, SD = 5.8) from 34 sports was examined. Athletes' skill level (Non-elite, pre-elite, or elite) were compared to familial characteristics and involvement in sport and physical activity using chi-square contingency tables or one-way ANOVA depending on variable type. Athlete skill level was associated with parent participation in each of the categories of physical activity examined (general fitness activities: p = .03, V = .18; recreational sport: p = .03, V = .18; competitive sport: p = .02, V = .18), and with sibling participation in all three categories combined (p = .04, V = .13). Athlete skill level was also associated with the highest level of competitive sport reached by parents (p < .01, ?b = .27) and siblings (p < .01, ?b = .22). Additionally, higher skilled athletes were more likely to be younger in birth order (adjusted standardized residual = 3.69). Moreover, the activity patterns of family members interacted to affect athlete development. This investigation extends the current understanding of how a family's physical activity and sport participation are related to athlete sport attainment, and reinforces the importance of family characteristics in the development of sport expertise.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".