Recovery for Professional and Elite Amateur Golfers: A Scoping Review of Evidence-Based Methods
Bibliographic record
Abstract
The lifestyle and athletic demands of a professional or elite amateur golfer are both physically and mentally challenging. Players need to withstand large forces during the swing, frequently travel between time zones, and often cycle through a variety of training and competition environments for large portions of the competitive season. Thus, with numerous factors contributing to physical and cognitive stress, optimising recovery for golfers is paramount. The primary objective of this scoping review was to evaluate different evidence-based recovery methods for professional and elite amateur golfers and assess where the current research gaps lie. A three-step search strategy identified relevant primary and secondary articles, in addition to the grey literature, using a total of five online databases (SPORTDiscus, Scopus, Web of Science, ProQuest Central and PubMed), which retrieved articles from January 2000 to May 2024. Data were extracted using a standardised tool to create a descriptive analysis and a thematic summary. Studies were included if they focused on nutritional and hydration methods, laboratory and controlled environment methods, sleep and jet leg management, independent methods or adjunct recovery methods, in relation to golf or other sporting populations. The initial search found 4862 relevant articles from the selected databases, with 39 studies meeting our criteria for the scoping review. Limited investigations have been conducted examining effective recovery methods for golfers. However, some preliminary evidence supports the use of targeted nutrition and hydration strategies, massage, and regular mobility and flexibility exercise. In addition, though, a more fundamental focus on sleep and jet lag management strategies is required, given the lifestyle challenges often faced by professional and elite amateur players. If golfers want to improve their chances of consistently competing at the highest level, strategies that focus on optimising recovery for superior health and well-being are essential for helping to sustain performance over time.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 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".