Fit for the fight? Illnesses in the Norwegian team in the Vancouver Olympic Games
Bibliographic record
Abstract
BACKGROUND: The development of strategies to prevent illnesses before and during Olympic Games provides a basis for improved health and Olympic results. OBJECTIVE: (1) To document the efficacy of a prevention programme on illness in a national Olympic team before and during the 2010 Vancouver Olympic Winter Games (OWG), (2) to compare the illness incidence in the Norwegian team with Norwegian incidence data during the Turin 2006 OWG and (3) to compare the illness incidence in the Norwegian team with illness rates of other nations in the Vancouver OWG. METHODS: Information on prevention measures of illnesses in the Norwegian Olympic team was based on interviews with the Chief Medical Officer (CMO) and the Chief Nutrition and Sport Psychology Officers, and on a review of CMO reports before and after the 2010 OWG. The prevalence data on illness were obtained from the daily reports on injuries and illness to the International Olympic Committee. RESULTS: The illness rate was 5.1% (five of 99 athletes) compared with 17.3% (13 out of 75 athletes) in Turin (p=0.008). A total of four athletes missed one competition during the Vancouver Games owing to illness, compared with eight in Turin. The average illness rate for all nations in the Vancouver OWG was 7.2%. Conclusions Although no definite cause-and-effect link between the implementation of preventive measures and the prevalence of illness in the 2010 OWG could be established, the reduced illness rate compared with the 2006 OWG, and the low prevalence of illnesses compared with other nations in the Vancouver OWG suggest that the preparations were effective.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".