Associations between Physical Activity, Screen Time, and Mental Health in Youth Hockey Parents
Bibliographic record
Abstract
Abstract Early sport specialization is becoming increasingly popular among youth. While the negative consequences of early specialization on the physical and psychological health of youth athletes have been well documented in the literature, less is known about the influence of youth sport participation on their parents. The purpose of this study was to examine the associations between physical activity, screen time and mental health in youth hockey parents, while considering the role of their child’s level of specialization. Youth hockey parents (N= 91; parents of early specializers n = 81; parents of late specializers n = 10), responded to an online survey. Results of independent samples t-tests indicated that parents of early specializers had poorer emotional well-being (p <.001) and engaged in more screen time (p = .006) compared to parents of late specializers. There were no significant differences in physical activity levels between groups; however, results showed that all parents were insufficiently active. This is concerning as hierarchal multiple regression revealed that physical activity positively predicted mental health (b = .26, p = .02), and specialization played a role (b = .24, p = .03). Findings suggest that early specialization has negative consequences on the health behaviours and mental health of youth hockey parents.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".