National Medical Teams during European Athletics Championships from 2009 To 2024: Composition, Gender Distribution, and Influence on Team Performance
Bibliographic record
Abstract
Abstract Background Having the overall goal to help countries/teams in the preparation of their national medical teams for international athletics championships, we aimed to describe the composition of national medical teams, including gender distribution, and to explore its potential association with team performance, during European Athletics championships. We conducted a retrospective study covering 15 consecutive outdoor and indoor European Athletics championships between 2009 and 2024 including the national medical team members and athletes registered. We extracted the number of national medical team members by profession and gender, the ratio of athletes per national medical team member, and the number of medals per athlete. Potential associations were explored using Spearman’s correlations. Results During the 15 consecutive European Athletics championships between 2009 and 2024, 54 European Athletics member federations participated at one or more of the championships, corresponding to 726 country-participations, from which 68.5% had a national medical team. The national medical team included: 71.0% physiotherapists and 29.0% physicians, 20.7% women and 79.3% men. There was a median of 11 (range: 1–43) athletes per physiotherapist and 23 (range: 3–64) athletes per physician. There was a small but significant negative correlation between the number of medals per athlete and the ratio of athletes per medical team member (r=-0.33; p < 0.001). Conclusions During the European Athletics championships, approximately two-thirds of countries/teams had a national medical team, with a median of eight athletes per medical team member, with large variation between teams. Only one out of five medical team members were women. When the number of athletes per medical team member was higher, this was associated with a lower number of medals per athlete. These findings may be of help to assemble effective and successful medical teams in future championships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.041 | 0.002 |
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; both teacher heads agree on what is shown here.
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".