Identifying and measuring the common elements of naturalistic developmental behavioral interventions for autism spectrum disorder: Development of the <i>NDBI-Fi</i>
Notice bibliographique
Résumé
Naturalistic developmental behavioral interventions for young children with autism spectrum disorder share key elements. However, the extent of similarity and overlap in techniques among naturalistic developmental behavioral intervention models has not been quantified, and there is no standardized measure for assessing the implementation of their common elements. This article presents a multi-stage process which began with the development of a taxonomy of elements of naturalistic developmental behavioral interventions. Next, intervention experts identified the common elements of naturalistic developmental behavioral interventions using quantitative methods. An observational rating scheme of those common elements, the eight-item NDBI-Fi, was developed. Finally, preliminary analyses of the reliability and the validity of the NDBI-Fi were conducted using archival data from randomized controlled trials of caregiver-implemented naturalistic developmental behavioral interventions, including 87 post-intervention caregiver–child interaction videos from five sites, as well as 29 pre–post video pairs from two sites. Evaluation of the eight-item NDBI-Fi measure revealed promising psychometric properties, including evidence supporting adequate reliability, sensitivity to change, as well as concurrent, convergent, and discriminant validity. Results lend support to the utility of the NDBI-Fi as a measure of caregiver implementation of common elements across naturalistic developmental behavioral intervention models. With additional validation, this unique measure has the potential to advance intervention science in autism spectrum disorder by providing a tool which cuts across a class of evidence-based interventions. Lay abstract Naturalistic developmental behavioral interventions for young children with autism spectrum disorder share key elements. However, the extent of similarity between programs within this class of evidence-based interventions is unknown. There is also currently no tool that can be used to measure the implementation of their common elements. This article presents a multi-stage process which began with defining all intervention elements of naturalistic developmental behavioral interventions. Next, intervention experts identified the common elements of naturalistic developmental behavioral interventions using a survey. An observational rating scheme of those common elements, the eight-item NDBI-Fi, was developed. We evaluated the quality of the NDBI-Fi using videos from completed trials of caregiver-implemented naturalistic developmental behavioral interventions. Results showed that the NDBI-Fi measure has promise; it was sensitive to change, related to other similar measures, and demonstrated adequate agreement between raters. This unique measure has the potential to advance intervention science in autism spectrum disorder by providing a tool to measure the implementation of common elements across naturalistic developmental behavioral intervention models. Given that naturalistic developmental behavioral interventions have numerous shared strategies, this may ease clinicians’ uncertainty about choosing the “right” intervention package. It also suggests that there may not be a need for extensive training in more than one naturalistic developmental behavioral intervention. Future research should determine whether these common elements are part of other treatment approaches to better understand the quality of services children and families receive as part of usual care.
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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 | 0,011 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| 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) | 0,001 | 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 ».