Injury Prevention in Youth Tackle Football
Notice bibliographique
Résumé
This thesis contains two projects that aim to investigate injury and injury prevention strategies in Canadian adolescent tackle football. The first project aimed to examine the current utilization of Neuromuscular Training components (NMT) in tackle football warm-ups and the second project examined adolescent (ages 14-17) tackle football epidemiology. Objectives: 1. To describe the current time spent by adolescent tackle football teams in five key neuromuscular training (NMT) components (aerobic, agility, balance and coordination, strength, and head on neck control) and determine if time in warm-up components differed throughout the season. 2. To describe injury rates, burden, types, mechanisms, and risk factors in adolescent (ages 14-17) community tackle football players in one season. Methods: Teams consented to video-recording of practice and game warm-ups. Video was analyzed using Dartfish tagging software (Dartfish, USA). Validated injury surveillance methods were used during a prospective cohort in a single nine-week competition season for participants aged 14-17. Injury rates (IR), concussion rates (CR), and incidence rate ratios (IRR) were reported based on univariable Poisson regression analyses (offset by player-hours and controlling for cluster by team). Results: Teams spent a median of 456.2 seconds in warm-up prior to sessions and a median time of 275 seconds in active warm-up components. Teams spent more time in some NMT components (aerobic and strength) compared to others (balance, agility and coordination, and head on neck control), however other than aerobic (58%) the use of other NMT components was low (time in NMT components 1-9%). Teams were relatively consistent with component utilization throughout the season. The overall IR was 4.61 injuries/1000 player-hours (95%CI; 3.84 – 5.53) and the CR was 1.20 concussions/1000 player-hours (95%CI; 0.90-1.61). Concussion rates were higher in games (IR=3.86 concussions/1000 player game-hours 95%CI; 2.74 – 5.43) than practices (IR=0.44 concussions/1000 practice player hours, 95%CI;0.25 – 0.75) (IRR=8.82,95%CI; 4.52- 18.27). Previous history of injury in the past 12 months (IRR=1.66,95%CI; 1.07-2.57) and being obese (BMI > 30.00) (IRR=2.55, 95%CI; 1.35-4.84) were associated with higher rates of practice-related injury. Lifetime history of concussion (IRR=1.58, 95%CI; 1.00 – 2.50) and being in the 75th percentile for height (IRR=1.58, 95%CI; 1.19 – 2.18) were associated with higher game-related injury rates, with the former being insignificant and the latter significant. Conclusions: Injury and concussion rates are high in adolescent tackle football. There are opportunities for research examining injury and concussion prevention strategies in tackle football in Canada. Football teams do not engage in NMT warm-up components and there is significant opportunity for implementation of such a prevention strategy in this sport.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».