Exploring Canadian Student-athletes’ perceptions of sleep and mental health
Bibliographic record
Abstract
Student-athletes face challenges in balancing academic, social, and athletic commitments, which can impact their mental health. While the importance of sleep for mental health is widely recognized, how it is perceived within the student-athlete population remains relatively unexplored. This study investigated the perceptions of Canadian post-secondary student-athletes regarding their sleep and mental health. In the fall of 2022, 115 participants (67% male, mean age 21.6 ± 2.5 years) completed an online survey including demographics, Pittsburgh Sleep Quality Index (PSQI), Sleep Hygiene Index (SHI), Mental Health Continuum Short Form (MHC-SF) and open-ended questions about their sleep experiences. The open-ended questions explored how participants determined the quality of their sleep and challenges to attaining optimal sleep. The PSQI findings indicated that 40% of participants averaged less than 7 hours of sleep/night, and 67% experienced poor sleep quality. Moreover, the SHI indicated that one of the highest-rated items was "I engage in activities before bedtime that may disrupt my sleep," indicating the lack of control over sleep. The MHC-SF scores indicated a broad range (0-24) of mental health states, with a mean score of 12.6 ± 7.3. Utilizing a qualitative description approach, open-ended responses were grouped into categories. The results revealed that 72.2% of student-athletes associated their sleep with their subsequent mental health and for 35.9% of student-athletes, sleep quality emerged as the primary challenge. Effectively addressing the sleep needs of student-athletes while fostering a shared responsibility approach among sport administrators, coaches, and athletes is crucial for their mental health and performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".