A Qualitative Analysis of the Role of Sleep Disorders in Sports Injuries Among Competitive Athletes in Canada
Bibliographic record
Abstract
Objective: This study aimed to explore how competitive athletes in Canada understand, experience, and cope with sleep-related challenges in relation to their injury histories. Methods: Using a qualitative approach, we conducted semi-structured interviews with 31 athletes aged 19 to 34, representing both individual and team sports. All participants had experienced at least one sports injury and reported issues with sleep. The interviews were analyzed in three stages—open, axial, and selective coding—allowing us to move from detailed personal accounts to broader thematic insights. Results: The analysis identified four interrelated themes. Disrupted Sleep Patterns captured athletes’ difficulties with initiating and maintaining sleep, commonly linked to stress, physical tension, and pre-sleep screen use. Emotional Burden of Sleep Loss highlighted the psychological toll of sleep disturbances, including heightened anxiety, irritability, and feelings of shame—particularly when performance declined. Heightened Injury Susceptibility reflected athletes’ perceptions that inadequate sleep contributed to greater injury risk and prolonged recovery. Finally, Systemic Barriers and Ineffective Coping pointed to a lack of institutional sleep education, cultural stigma surrounding rest, and reliance on unhelpful self-management strategies, which often compounded the problem. Conclusion: Sleep issues in elite athletes are not just about feeling exhausted—they reach into deeper layers of well-being. Poor sleep affects not only how athletes perform, but also how they feel, recover, and even how they define themselves. Hearing these experiences directly from athletes themselves reveals an urgent need for more compassionate and informed support systems—ones that include meaningful sleep education, accessible mental health care, and a cultural shift in sports that values rest and recovery just as much as effort and endurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".