Global social support and social relatedness in physical activity are independent predictors of increased mental health and reduced anxiety symptoms among young adults
Bibliographic record
Abstract
INTRODUCTION: Social support and social relatedness are widely recognized to have a powerful effect on physical as well as mental health. Many interventions including physical activity (PA) can improve social determinants of mental health. This research aimed to investigate if general social support and social relatedness specific to the physical activity context impacts positively on mental health and negatively on anxiety and depression among youth. METHODS: A total of 1527 students (58% female; mean age = 18.4 years, SD = 2.4) completed questionnaires at baseline; 460 completed follow-up questionnaires 6 months later (Quebec, Canada). Multivariate linear regressions were performed to model the associations between global social support and social relatedness in PA at baseline and mental health, anxiety and depressive symptoms at follow-up controlling for sex, age, perceived socioeconomic status, PA volume and mental health/disorders symptoms at baseline. RESULTS: Global social support and social relatedness in PA were independent predictors of increased mental health (respectively ? (95%CI)= .18 (.04, .33) and ? (95%CI)=. 25 (.06, .44)), and decreased anxiety symptoms (respectively ? (95%CI)= -.06 (-.11, -.02) and ? (95%CI)= -.07 (-.13, -.02)). Only global social support was a significant predictor of decreased depressive symptoms (? (95%CI)= -.09 (-.14, -.04)) CONCLUSION: These results suggest that both global social support and social relatedness in PA are predictors of mental health an anxiety symptoms. Interventions aiming at promoting mental health and preventing anxiety disorders among youth should not only target social support but also focus on enhancing social relatedness in the specific context of PA.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".