Musculoskeletal Injuries Among Females in the Military: A Scoping Review
Notice bibliographique
Résumé
INTRODUCTION: Musculoskeletal injuries (MSKi) are a common challenge for those in military careers. Compared to their male peers, reports indicate that female military members and recruits are at greater risk of suffering MSKi during training and deployment. The objectives of this study were to identify the types and causes of MSKi among female military personnel and to explore the various risk factors associated with MSKi. MATERIALS AND METHODS: A scoping review was conducted over a 4-month time frame of English language, peer-reviewed studies published from 1946 to 2019. Search strategies for major biomedical databases (e.g., MEDLINE; Embase Classic + Embase; and the following EBM Reviews-Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment, and the NHS Economic Evaluation Database) were developed by a senior medical information specialist and included 2,891 titles/abstracts. Study selection and data collection were designed according to the Population, Concept, and Context framework. Studies were included if the study population provided stratified data for females in a military context. RESULTS: From a total of 2,287 citations captured from the literature searches, 168 peer-reviewed publications (144 unique studies) were eligible for inclusion. Studies were identified from across 10 countries and published between 1977 and 2019. Study designs were primarily prospective and retrospective cohorts. Most studies assessed both prevalence/incidence and risk factors for MSKi (62.50%), with few studies assessing cause (13.69%). For MSKi of female recruits compared to active female members, the prevalence was higher (19.7%-58.3% vs. 5.5%-56.6%), but the incidence (0.02%-57.7% vs. 13.5%-71.9%) was lower. The incidence of stress fractures was found to be much higher in female recruits than in active members (1.6%-23.9% vs. 2.7%). For anthropometric risk factors, increased body fat was a predictor of MSKi, but not stress fractures. For physiological risk factors for both female military groups, being less physically fit, later menarche, and having no/irregular menses were predictors of MSKi and stress fractures. For biomechanical risk factors, among female recruits, longer tibial length and femoral neck diameter increased the risk of stress fractures, and low foot arch increased risk of an ankle sprain. For female active military members, differences in shoulder rotation and bone strength were associated with risk of MSKi. For biological sex, being female compared to male was associated with an increased risk of MSKi, stress fractures, and general injuries. The consequences of experiencing MSKi for active military included limited duties, time off, and discharge. For recruits, these included missed training days, limited duty days, and release. CONCLUSIONS: This scoping review provides insight into the current state of the evidence regarding the types and causes of MSKi, as well as the factors that influence MSKi among females in the military. Future research endeavors should focus on randomized controlled trials examining training paradigms to see if women are more susceptible. The data presented in the scoping review could potentially be used to develop training strategies to mitigate some of the identified barriers that negatively impact women from pursuing careers in the military.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 tête enseignante, 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 ».