Examination of clinical sleep difficulties in college student-athletes
Bibliographic record
Abstract
BACKGROUND: Due to the rising concern of inadequate sleep, critical analysis is needed for the presence of sleep problems in diverse populations. Research has shown that college athletes may be one such population at risk for sleep disturbances. Poor sleep may lead to physiological, psychological, and cognitive deficits that can impact college athletes academically and athletically. This investigation was performed to examine the relationship of age, sex, and history of concussion on sleep disturbance in college athletes. METHODS: A total of 191 collegiate athletes between the ages of 18-26 from a single academic NCAA institution in the Rocky Mountain region of the USA, consented to participate in the study. Participants completed a demographic questionnaire and the Athlete Sleep Screening Questionnaire (ASSQ). Results were analyzed using SPSS Version 27. RESULTS: Primary results revealed that female athletes reported higher sleep disturbance scores when compared to males (U=3643.0, P=0.016). Self-reported sleep disturbances when traveling for sport were higher for females (X(1) = 23.800, P<0.001). Males were also less likely to report daytime dysfunction when traveling for sport (X(1) =22.988, P<0.001). Sleep disturbance had a significant association with age (X(1) =4.145, P=0.042), with older participants (20+ years of age) reporting greater sleep disturbance. Concussion history did not associate with sleep disturbance in the present study. CONCLUSIONS: Results suggest that sophomore or older female may be at higher risk for sleep disturbances. Clarifying sex-specific sleep health and understanding the role of age and academic class is crucial to enhance and personalize interventions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".