Investigating the influence of youth hockey specialization on psychological needs (dis)satisfaction, mental health, and mental illness
Bibliographic record
Abstract
The Developmental Model of Sport Participation describes three pathways that youth can follow: recreational participation, late specialization and early specialization. Many competitive sport programmes are promoting early specialization in hopes that their athletes will gain an advantage over others; however, research indicates that youth who wait until adolescence to specialize in a given sport may achieve physical and psychological benefits. The purpose of this study was to investigate the psychological effects of sport specialization by examining relationships between youth hockey players’ level of specialization, psychological needs satisfaction (PNS), psychological needs dissatisfaction (PND), mental health and mental illness. Sixty-one youth male hockey players (Mage = 14.90) responded to an online survey. Results indicated that PND according to specialization was significant with early specializers reporting the highest PND and recreational athletes reporting the lowest PND (p = .029), indicating a large effect size (η2 = .157). No other significant differences were found. Bivariate correlations revealed significant relationships between all variables. Moreover, regression analyses showed that PNS positively predicted mental health (β = .47) and negatively predicted mental illness (β = −.51), while PND positively predicted mental illness (β = .71) and negatively predicted mental health (β = −.44). Results suggest that PNS is important to promote mental health and avoid mental illness. Future research is needed to fully understand the psychological consequences of early sport specialization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".