0094 An investigation of risk & protective factors for school-aged child injuries: the influence of siblings
Notice bibliographique
Résumé
<h3>Statement of purpose</h3> Previous research has identified a variety of risk and protective factors for injuries in school-aged children, including age, sex, number of siblings, and child risk taking behaviors. The goal of the present study was to go beyond these known risk factors and investigate if and how siblings influence the frequency and severity of childhood injuries. <h3>Methods/Approach</h3> Seventy-nine families with two school-aged children aged seven and ten years old on average were recruited from the community; 54% were female. Parents were 38 years old on average and self-identified as multiracial (8%), Indigenous (18%), and European-Canadian (75%); 92% were female. Parents reported on the frequency of minor and medically-attended child injuries within the past three months. They also reported on child risk-taking behavior and sibling supervision. Children reported on warmth and hostility in their sibling relationships. <h3>Results</h3> Larger sibling spacing but not number of siblings was significantly associated with more minor injuries for younger siblings. Boys with older brothers experienced significantly more medically-attended injuries than boys or girls with older sisters. Greater risk taking was related to significantly more minor injuries for both younger and older siblings. Younger but not older sibling minor injuries were negatively related to sibling warmth and positively related to sibling hostility. Sibling supervision was not associated with injury frequency or severity, but was negatively related to warmth reported by both younger and older siblings. <h3>Conclusions</h3> In addition to well-known demographic characteristics, siblings played an influential role in both elevating and mitigating injury risk for school-aged children, with older siblings having a greater influence on safety. <h3>Significance</h3> Siblings are often not taken into account in injury research, their influence on child safety is understudied. The present study highlighted some of the important aspects of sibling influence that could inform future injury prevention programs.
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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».