Investigating the impact of youth hockey specialization and psychological needs (dis) satisfaction on mental health
Bibliographic record
Abstract
There has been a wealth of research in recent years on the positive and negative aspects of youth sport participation. The Developmental Model of Sport Participation (Côté & Fraser-Thomas, 2007) describes three separate pathways that youth can follow in their development: recreational participation, late specialization and early specialization. Many competitive sport programs 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 achieve greater performance (Moesch et al., 2011), experience less burnout and injury (Jayanthi et al., 2013) and less emotional stress (Gould, 2010). Therefore, the purpose of this study was to examine the relationships between youth hockey players' level of specialization, psychological need satisfaction (PNS) and dissatisfaction (PND), mental health and mental illness. Sixty one youth hockey players responded to online surveys composed of validated scales via FluidSurveys. Results indicated a significant difference between PNS according to specialization with early specializers reporting the lowest PNS and recreational athletes reporting the highest PNS (F = 6.28, p = .001). Further findings revealed a positive medium correlation between PNS and mental health (r = .39, p = .002) and a positive strong correlation between PND and mental illness (r = .65, p < .0005). There were also medium and negative correlations between PND and mental health (r = -.42, p = .001) and between PNS and mental illness (r = -.48, p < .0005). These results suggest that sport specialization may have an impact on psychological need (dis)satisfaction which is related to mental health and mental illness.
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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 | 0.000 | 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) | 0.003 | 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".