The Relationship Between Resilience and Well-being in Post-secondary Student-Athletes and Non-Athletes
Bibliographic record
Abstract
Post-secondary student-athletes are exposed to a unique combination of stressors that can affect their well-being. Studies have demonstrated that factors such as physically and psychologically demanding competitions, expectations to perform in the classroom and on the field, and time demands may result in greater mental health concerns than the general student population. Research has shown that resilience may lower levels of stress, anxiety, and depression and reduce the development of burnout. However, limited research has examined how mental well-being is associated with resilience in a student-athlete population. The purpose of this study was to investigate the relationships among resilience and mental well-being in student-athletes and non-athletes within post-secondary institutions across Canada. Data were analyzed from the 2019 Canadian Campus Well-being Survey (CCWS), which consisted of 40,720 university and college students (1853 varsity student-athletes, 38,867 non-athletes). The CCWS includes the Warwick-Edinburgh Mental Well-being Scale (Tennant et al., 2007) and a custom resilience measure that assesses individuals’ ability to handle unexpected and difficult situations. A mediational analysis revealed that resilience mediated the relationship between student status (i.e., student-athlete vs. non-athletes) and well-being. This finding suggests that mental health differences seen between student-athletes and non-athletes may be due to resilience, which has been shown to be developed through sports participation (Fletcher & Sarkar, 2012). Since resilience may be a resource that contributes to well-being, there is value for post-secondary institutions and university athletic departments to implement resilience education programs that provide students with intentional strategies to foster resilience and associated life skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".