Fitness-related self-conSCIous emotions as mediators for the association between athletes' fitness perceptions and depression during the COVID-19 pandemic
Bibliographic record
Abstract
Depression among athletes has emerged as a significant outcome of the COVID-19 pandemic. Understanding experiences of depression symptoms is important as many athletes are unable to train normally for their respective sports due to the pandemic. Consequently, many athletes may perceive their fitness and training has suffered, which has implications on mental health. Fitness-related self-conscious emotions (i.e., shame, guilt, authentic pride, and hubristic pride) may help to explain the relationship between these fitness perceptions and mental health. The present study explores fitness-related self-conscious emotions as mediators for the association between athletes' perceptions of fitness and levels of depression during the COVID-19 pandemic. Varsity athletes (N = 125) from the University of Toronto completed a cross-sectional self-report survey in a major lockdown and nearly a year after the beginning of the pandemic. Controlling for age and gender, separate mediation models reveal significant indirect effects of fitness perceptions on depression through shame, b = -.07, 95% CI(-.14, -.01), p
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".