Exploring competitive anxiety and personality in early specializng and sampling peewee boys hockey players
Bibliographic record
Abstract
In recent years there has been a growing trend towards early specialization in youth sport (Feeley et al., 2016). Athletes who specialize early often invest more heavily from a younger age, thus it has been suggested they may feel greater pressures to perform, and have higher anxiety levels (Baker et al., 2009). Personality has also been linked to anxiety (e.g. Malouff, Thorstiensson & Schutte, 2004), but no research has focused specifically on the relationship between anxiety, personality, and sport. Using the Developmental Model of Sport Participation (DMSP; Côté & Fraser -Thomas, 2016) as a guiding framework, this study examined the relationship between competitive anxiety and personality, in relation to sport trajectory (i.e., early specializer versus sampler). Seventy-seven male hockey players aged 11-12 completed tools to measure competitive anxiety and personality, while their parents completed a screening tool on boys' sport development history. Hierarchal regression analyses revealed an interaction between agreeableness and early specialization, indicating that samplers who had low agreeableness reported significantly higher levels of competitive state anxiety. Findings provide preliminary information about who early specialization may be best suited for; however, further research in different sport contexts is needed to offer insight to programmers, parents, and youths regarding decisions about children's sport pathways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".