Identifying and measuring the common elements of naturalistic developmental behavioral interventions for autism spectrum disorder: Development of the <i>NDBI-Fi</i>
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".