90 School-based Implementation of Educational and Neurocognitive Interventions in Children with Neurodevelopmental Disorders.
Notice bibliographique
Résumé
Objective: Children with neurodevelopmental disorders (NDDs) commonly experience attentional and executive function (EF) difficulties that are negatively associated with academic success, psychosocial functioning, and quality of life. Access to early and consistent interventions is a critical protective factor and there are recommendations to deliver cognitive interventions in schools; however, current cognitive interventions are expensive and/or inaccessible, particularly for those with limited resources and/or in remote communities. The current study evaluated the school-based implementation of two game-based interventions in children with NDDs: 1) a novel neurocognitive attention/EF intervention (Dino Island; DI), and 2) a commercial educational intervention (Adventure Academy; AA). DI is a game-based attention/EF intervention specifically developed for children for delivery in community-based settings. Participants and Methods: Thirty five children with NDDs (ages 5-13 years) and 17 EAs participated. EAs completed on-line training to deliver the interventions to assigned students at their respective schools (3x/week, 40-60 minutes/session, 8 weeks, 14 hours in total). We gathered baseline child and EA demographic data, completed pre-intervention EA interviews, and conducted regular fidelity checks throughout the interventions. Implementation data included paper-pencil tracking forms, computerized game analytic data, and online communications. Results: Using a mixed methods approach we evaluated the following implementation outcomes: fidelity, feasibility, acceptability, and barriers. Overall, no meaningful between-group differences were found in EA or child demographics, except for total number of years worked as an EA (M = 17.18 for AA and 9.15 for DI; t (22) = - 4.34, p < .01) and EA gender (χ2 (1) = 6.11, p < .05). For both groups, EA age was significantly associated with the number of sessions played [DI (r = .847, p < .01), AA (r = .986, p < .05)]. EAs who knew their student better completed longer sessions [DI (r = .646), AA (r = .973)], all ps < .05]. The number of years worked as an EA was negatively associated with the total intervention hours for both groups. Qualitative interview data indicated that most EAs found DI valuable and feasible to deliver in their classrooms, whereas more implementation challenges were identified with AA. Barriers common to both groups included technical difficulties (e.g., game access, internet firewalls), environmental barriers (e.g., distractions in surroundings, time of the year), child factors (e.g., lack of motivation, attentional difficulties, frustration), and game-specific factors (e.g., difficulty level progression). Barriers specific to DI included greater challenges in motivating children as a function of difficulty level progression. Furthermore, given the comprehensive nature of training required for delivery, EAs needed a longer time to complete the training for DI. Nevertheless, many EAs in the DI group found the training helpful, with a potential to generalize to other children in the classroom. Conclusions: The availability of affordable, accessible, and effective cognitive intervention is important for children with NDDs. We found that delivery of a novel cognitive intervention by EAs was feasible and acceptable, with similarities and differences in implementation facilitators/barriers between the cognitive and commercialized academic intervention. Recommendations regarding strategies for successful school-based implementation of neurocognitive intervention will be elaborated on in the poster.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».