A longitudinal study on quality of life after injury in children
Notice bibliographique
Résumé
BACKGROUND: In high income countries, injuries account for 40 % of all child deaths, representing the leading cause of child mortality and a major source of morbidity. The need for studies across age groups, and use of health related quality of life measures that assess functional limitations in multiple health domains, with sampling at specific post-injury time points has been identified. The objective of this study was to describe the impact of childhood injury and recovery on health related quality of life (HRQoL) for the 12 months after injury. METHODS: In this prospective cohort study parents of children 0-16 years old attending British Columbia Children's Hospital for an injury were surveyed over 12 months post-injury. Surveys assessed HRQoL at four points: baseline (pre-injury), one month, four to six months and 12 months post injury. Generalized estimating equation models identified factors associated with changes in HRQoL over time. RESULTS: A total of 256 baseline surveys were completed. Response rates for follow-ups at one, four and twelve months were 74 % (186), 67 % (169) and 64 % (161), respectively. The mean age of participants was 7.9 years and 30 % were admitted to the hospital. At baseline, a retrospective measure of pre-injury health, the mean HRQoL score was 90.7. Mean HRQoL ratings at one, four and 12 months post injury were 77.8, 90.3 and 91.3, respectively. Both being older and being hospitalized were associated with a steeper slope to recovery. CONCLUSIONS: Although injuries are prevalent, the long term impacts of most childhood injuries are limited. Regardless of injury severity, most injured children recuperated quickly, and had regained total baseline status by four month post-injury. However, although hospitalization did not appear to impact long term psychosocial recovery, at four and 12 months post injury a greater proportion of hospitalized children continued to have depressed physical HRQoL scores. Both older and hospitalized children reported greater impact to HRQoL at one month post injury, and both had a steeper slope to recovery and were on par with their peers by four month.
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 | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».