Teamwork Makes the Dream Work: Who Should Be Managing Athletes on Matters Related to Sleep?
Bibliographic record
Abstract
Optimising sleep health is essential for athlete recovery and performance, but responsibility for managing sleep in high-performance sports is often unclear. Although performance support teams include diverse professionals such as coaches, sport scientists, physiotherapists, sports dietitians, psychologists, and physicians, guidance may be fragmented and inconsistent across training environments and competition schedules. This paper proposes a collaborative multidisciplinary model in which sleep specialists may integrate with existing support staff to deliver unified, evidence-based sleep strategies throughout all phases of athlete preparation. By fostering open communication channels, aligning screening protocols, and coordinating interventions, this model ensures consistent messaging and implementation of sleep initiatives. We also address sleep monitoring via wearable technologies, highlighting device performance and data confidentiality considerations to ensure accurate and ethical use of athlete sleep metrics. Emphasis is placed on creating organisation-wide policies that recognise sleep as an important component to health and performance. Adopting this integrated approach to sleep may enhance overall physical and mental health, reduce injury risk, improve recovery, and ultimately, enhance athletic performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".