An Uneven Playing Field: Athlete Injury, Illness, Load, and Daily Training Environment in the Year Before the FINA (Aquatics) World Championships, 2017
Bibliographic record
Abstract
OBJECTIVE: To assess athlete's training environment and health problems before the FINA World Championships (WC) (2017) and to analyze the differences between the 6 disciplines, gender, and countries with different medal rankings during the championships. DESIGN: Retrospective anonymous questionnaire. SETTING: FINA WC 2017. PARTICIPANTS: Registered aquatic athletes (swimmers, divers, high divers, water polo players, artistic swimmers, open water swimmers). MAIN OUTCOME MEASURES: The outcome measures included the following: training and competition load, availability of support staff, performance of injury prevention exercises, and prevalence of health problems. The independent variables included gender, aquatic discipline, and country group based on medal ranking. RESULTS: In the 12 months preceding the Championships, 67% of the athletes reported physical complaints and 41% trained or competed with a diagnosed injury. Only half of the athletes reported that injury prevention exercises were always (29.9%) or often (23.4%) a regular part of their daily training. In the daily training environment, support staff (excluding coach) was not available or available only if the athlete pays in 28.4% (specialized trainer) to 58.9% (sport scientist) of cases. About one-quarter of the athletes rated the support offered by their National Team program as poor or very poor. There were discrepancies in training loads and support staff among the 6 aquatic disciplines. Availability of support staff, athlete's satisfaction with their training environment/support, and regular use of injury prevention exercises were higher in countries that ranked higher in the medal list. CONCLUSIONS: Injury prevention strategies should be promoted in aquatic sports, with prospective surveillance for the early identification of physical complaints. The facilitation of access to sport-specific experts could improve athlete's health and performance, especially in countries with low medal ranking.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".