Reliability and Confirmatory Factor Analysis (CFA) of a Paper- Versus App-Administered Resilience Scale in Scottish Youths: Comparative Study
Notice bibliographique
Résumé
Background Adequately measuring resilience is important to support young people and children who may need to access resources through social work or educational settings. A widely accepted measure of youth resilience has been developed previously and has been shown to be suitable for vulnerable youth. While the measure is completed by the young person on paper, it has been designed to be worked through with a teacher or social worker in case further clarification is required. However, this method is time consuming and, when faced with large groups of pupils who need assessment, can be overwhelming for schools and practitioners. This study assesses app software with a built-in avatar that can guide young persons through the assessment and its interpretation. Objective Our primary objective is to compare the reliability and psychometric properties of a mobile software app to a paper version of the Child and Youth Resilience measure (CYRM-28). Second, this study assesses the use of the CYRM-28 in a Scottish youth population (aged 11-18 years). Methods Following focus groups and discussion with teachers, social workers, and young people, an avatar was developed by a software company and integrated into an android smartphone app designed to ask questions via the device’s inbuilt text-to-voice engine. In total, 714 students from 2 schools in North East Scotland completed either a paper version or app version of the CYRM-28. A cross-sectional design was used, and students completed their allocated version twice, with a 2-week period in between each testing. All participants could request clarification either from a guidance teacher (paper version) or from the in-built software glossary (app version). Results Test and retest correlations showed that the app version performed better than the paper version of the questionnaire (paper version: r303=0.81; P<.001; 95% CI 0.77-0.85; app version: r413=0.84; P<.001; 95% CI 0.79-0.89). Fisher r to z transformation revealed a significant difference in the correlations (Z=–2.97, P<.01). Similarly, Cronbach α in both conditions was very high (app version: α=.92; paper version: α=.87), suggesting item redundancy. Ordinarily, this would lead to a possible removal of highly correlated items; however, our primary objective was to compare app delivery methods over a pen-and-paper mode and was hence beyond the scope of the study. Fisher r to z transformation revealed a significant difference in the correlations (Z=–3.69, P<.01). A confirmatory factor analysis supported the 3-factor solution (individual, relational, and contextual) and reported a good model fit (χ215=27.6 [n=541], P=.24). Conclusions ALEX, an avatar with an integrated voice guide, had higher reliability when measuring resilience than a paper version with teacher assistance. The CFA reports similar structure using the avatar when compared against the original validation.
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 | 0,014 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».