Is psychological resilience associated with changes in youth sport participants’ health-related quality of life during the COVID-19 pandemic?
Bibliographic record
Abstract
Objectives: This study investigated the quality of life of youth sport participants over the COVID-19 pandemic as moderated by psychological resilience.Methods: Participants included 93 high school sport participants (53.76% female, mean age = 15.59 ± 0.74) in a three-year longitudinal cohort study (SHRed Concussions) who completed the Connor–Davidson Resilience Scale (CD-RISC), Pediatric Quality of Life Scale (PedsQL), and Strengths and Difficulties Questionnaire (SDQ) at Year 1 (pre-pandemic, 2019–2020) and Year 2 (pandemic, 2020–2021). Change in quality of life and mental health symptoms from Year 1 to Year 2 was examined using paired t-tests and Year 1 resilience was examined as a predictor of Year 2 quality of life and mental health symptoms using linear regression.Results: Among participants with Year 1 scores before the pandemic onset, mean PedsQL (n = 74, t = −0.26 [−2.63, 2.03], p = 0.80) and SDQ (n = 74, t = 0.030 [−0.90, 0.93], p = 0.98) scores did not significantly change between Year 1 and Year 2. In unadjusted analyses, Year 1 CD-RISC scores were positively associated with predicted Year 2 PedsQL scores when Year 1 scores were controlled (β = 0.31 [0.0062, 0.61], ΔR2 = 0.02) but not with residual change in SDQ scores (β = 0.035 [−0.11, 0.18], ΔR2 = 0.001).Conclusions: Quality of life did not change significantly after the pandemic onset, and resilience was modestly protective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".