Psychosocial development and mental health in youth Brazilian club athletes: examining the effects of age, sport type, and training experience
Bibliographic record
Abstract
Abstract Physical activity occurring through organized sport has been positioned as an engaging manner not only to prevent chronic-degenerative diseases but also to promote healthier societies. However, there is a lack of evidence linking competitive sport participation in the club environment in promoting youth athletes’ psychosocial development and mental health. Thus, this study aimed to analyze the effects of age, sport type, and training experience on the psychosocial development and mental health of youth Brazilian club athletes. Participants were 220 male adolescent athletes (Mean =14.09 years; SD = 2.21) from individual and team sports. Instruments included the Portuguese Youth Experience Survey for Sport (P-YES-S) and the Portuguese Mental Health Continuum – Short Form (P-MHC-SF). Correlation and multilevel linear regression analyses were performed. The results indicated a moderated correlation between both questionnaires. For the P-YES-S, model effect estimations showed variation for age in the Personal and Social Skills dimension and variations for training experience in the Cognitive Skills and Negative Experiences dimensions. For the P-MHC-SF, model effect estimations showed variation for age in the Emotional Well Being dimension and variation for sport type in Social Well Being and Psychological Well Being dimensions. More research is needed to continue examining how characteristics of sport participation are related to psychosocial development and mental health.
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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 | 0.001 |
| 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 | 0.000 |
| 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".