Assessing resiliency in paediatric rehabilitation: A critical review of assessment tools and applications
Notice bibliographique
Résumé
BACKGROUNDS: Resiliency has attracted a growing interest in paediatric rehabilitation as a key capacity for disabled children and their families to thrive. This study aimed to identify measures used to assess resiliency of disabled children/youth and their families and critically appraise the current use of resiliency measures to inform future research and practice. METHODS: A two-stage search strategy was employed. First, systematic reviews of resiliency measures published since 2000 were searched. Second, full names of measures identified in at least two systematic reviews were searched across four electronic databases. Included studies assessed resiliency among children/youth (0-18 years old) with chronic health conditions and/or disabilities and their families. Identified articles were then analysed to discern the study's definition of resiliency, authors' rationales for measurement selection, and types of perceived adversities facing the study participants. RESULTS: From an initial yield of 25 measures identified in five systematic reviews, 11 were analysed in two or more reviews. The second stage yielded 41 empirical studies published between 2012 and 2018, which used 8 of the 11 resiliency measures searched by name. Of 41, 17 studies measured resiliency of disabled children/youth, 23 assessed resiliency within family members, and 1 studied both children/youth and their families. Our critical appraisal identified inconsistencies between the studies' definition of resiliency and chosen measures' operationalization, implicit assumption of disabilities as a developmental risk that automatically results in life adversities, and the tendency among family studies to reduce resiliency down to stress coping skills. Research that encompasses contextual factors and developmental influences is lacking. CONCLUSIONS: There is a need for a situated measurement approach that captures multiple interacting factors shaping resiliency over one's life course. Resiliency measures would benefit from a greater focus on a person-environment transaction and an alternative definition of resiliency that accounts for multiple capacities to navigate through disabling environments.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».