Impact Of Covid-19 Pandemic On Injury And Illness In Canada Games Competition
Notice bibliographique
Résumé
Participation in elite level competition, such as Canada Games (CG), encourages large scale participation in Summer and Winter sport events; however, there are inherent injury risks, which may have been magnified by activity restrictions imposed during the COVID-19 pandemic lockdown. PURPOSE: To compare if injury/illness characteristics, incidence, and odds of injury for 2022 CG were different from pre-pandemic CG. METHODS: De-identified data from 2022 and pre-pandemic CG (2009 - 2019) were categorized based on injury area and type, and acute/chronic injury; illness was categorized by affected system. Frequency of injured body area, type, acute/chronic, and illness were calculated as a percentage of total injuries/illnesses. Incidence of injury/illness was calculated per 1000 athletes. Odds ratio (OR [95% CI]) for injury/illness were calculated for differences between 2022 and pre-pandemic CG. Microsoft Excel was used for analysis with p < 0.05 for statistical significance. RESULTS: There were 1955 male (M) and 1786 female (F) athletes participating in 2022 CG; pre-pandemic CG averaged 1819 M and 1571 F athletes. In 2022 CG, thigh was most frequently injured (n = 151; 12.3%), strains were most common (n = 538; 46.9%), most injuries were overuse (n = 664; 57.8%), and other category was most often affected illness system (n = 38; 34.2%). In pre-pandemic CG, shoulder was most frequently injured (n = 368; 10.0%), sprains were most common (n = 1444; 33.7%), most injuries were acute (n = 1854; 50.6%), and other category was most often affected illness (n = 142; 29.5%). Injury incidence was 306.87 and 360.31 and illness incidence was 29.67 and 47.30 per 1000 athletes in 2022 CG and pre-pandemic CG, respectively. Athletes competing in 2022 CG had significantly lower odds of injury (OR = .79 [.73 - .85]) and illness (OR = .62 [.50 - .76]) compared to pre-pandemic CG. CONCLUSION: 2022 CC participants had lower incidence and odds of injury/illness than pre-pandemic CG; however, there were differences in injury characteristics. Suggesting that, although pandemic restrictions may have had a protective effect, there were more chronic injuries, and the body area and injury type differed from pre-pandemic CG. Supported by a Brock University Canada Games Grant; Acknowledgement: Canada Games Council for providing de-identified data
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».