Wake up call for collegiate athlete sleep: narrative review and consensus recommendations from the NCAA Interassociation Task Force on Sleep and Wellness
Bibliographic record
Abstract
Sleep is an important determinant of collegiate athlete health, well-being and performance. However, collegiate athlete social and physical environments are often not conducive to obtaining restorative sleep. Traditionally, sleep has not been a primary focus of collegiate athletic training and is neglected due to competing academic, athletic and social demands. Collegiate athletics departments are well positioned to facilitate better sleep culture for their athletes. Recognising the lack of evidence-based or consensus-based guidelines for sleep management and restorative sleep for collegiate athletes, the National Collegiate Athletic Association hosted a sleep summit in 2017. Members of the Interassociation Task Force on Sleep and Wellness reviewed current data related to collegiate athlete sleep and aimed to develop consensus recommendations on sleep management and restorative sleep using the Delphi method. In this paper, we provide a narrative review of four topics central to collegiate athlete sleep: (1) sleep patterns and disorders among collegiate athletes; (2) sleep and optimal functioning among athletes; (3) screening, tracking and assessment of athlete sleep; and (4) interventions to improve sleep. We also present five consensus recommendations for colleges to improve their athletes’ sleep.
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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 | 0.045 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".