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Record W4407748035 · doi:10.1177/15407969251319255

Evaluating the Long-Term Efficacy of a Trauma-Informed Approach to Addressing Challenging Behavior in the Home

2025· article· en· W4407748035 on OpenAlexaff
Aaron Leyman, Phoebe MacDowell, Joshua Jessel

Bibliographic record

VenueResearch and Practice for Persons with Severe Disabilities · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychological interventionContingencyAffect (linguistics)Functional analysisPsychologyTolerationProcess (computing)Applied behavior analysisApplied psychologyComputer scienceRisk analysis (engineering)Developmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.606
GPT teacher head0.553
Teacher spread0.053 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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