Evaluating the Long-Term Efficacy of a Trauma-Informed Approach to Addressing Challenging Behavior in the Home
Bibliographic record
Abstract
Behavioral interventions for challenging behavior often rely on the results of a functional analysis to identify environmental contributors. Multiple functional analysis formats have been developed to improve qualities of the process such as practicality, efficiency, and safety. More recently, the performance-based, interview-informed synthesized contingency analysis (IISCA) was developed as a functional analysis format that incorporates a trauma-informed framework. The performance-based IISCA (a) introduces evocative events following periods of calm to reduce dangerous escalation, (b) includes moment-to-moment measures of challenging behavior to allow for ongoing visual analysis of data, and (c) maintains measures of positive affect. We conducted this study to evaluate the treatment utility of the performance-based IISCA when it is used to inform a skill-based treatment. The performance-based IISCA was conducted for the challenging behavior of three autistic children before teaching communication, toleration, and cooperation during skill-based treatment in the home setting. Challenging behavior was reduced for all participants across different therapists and across time (1-, 2-, 3-month treatment extension). The results support the extension and longevity of treatment informed by the performance-based IISCA.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".