The Recovery Umbrella in the World of Elite Sport: Do Not Forget the Coaching and Performance Staff
Bibliographic record
Abstract
In the field of sports science, the recovery umbrella is a trending topic, and even more so in the world of elite sports. This is evidenced by the significant increase in scientific publications during the last 10 years as teams look to find a competitive edge. Recovery is recognized to be an integral component to assist athlete preparation in the restoration of physical and psychological function, and subsequently, performance in elite team sports athletes. However, the importance of recovery in team staff members (sports coaches and performance staff) in elite sports appears to be a forgotten element. Given the unrelenting intense nature of daily tasks and responsibilities of team staff members, the elite sports environment can predispose coaches to increased susceptibility to psycho-socio physiological fatigue burden, and negatively affect health, wellbeing, and performance. Therefore, the aim of this opinion was to (1) develop an educational recovery resource for team staff members, (2) identify organizational task-specific fatigue indicators and barriers to recovery and self-care in team staff members, and (3) present recovery implementation strategies to assist team staff members in meeting their organizational functions. It is essential that we do not forget the coaching and performance staff in the recovery process.
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 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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".