Sport Participation, Extracurricular Activity Involvement, and Psychological Distress: A Latent Class Analysis of Canadian High School Student-Athletes
Bibliographic record
Abstract
Although psychological distress has been shown to increase during adolescence, participation in organized activities may have protective effects. The present study aimed to identify whether there is a relationship between high school student-athletes’ breadth of participation in organized activities and psychological distress, using a latent class analysis. Canadian adolescent-athletes (n = 930) in Grades 11 and 12 completed an online survey that measured: (a) high school sport participation, (b) community sport participation, (c) nonsport extracurricular activities participation, and (d) psychological distress. The latent class analysis indicated that a two-class model (i.e., Class 1 = narrower breadth, low distress; Class 2 = wider breadth, moderate distress) was most appropriate. Results indicated that despite the divergent probability of organized activity participation, participants in both classes had a low to moderate probability of presenting elevated levels of psychological distress. However, levels of psychological distress were still higher than other Canadian adolescent populations, suggesting that overscheduling could be of concern. Gender and time (i.e., prior/during COVID-19 pandemic) were significant covariates in the model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".