The Performance-based IISCA Can Inform Effective and Socially Meaningful Skill-based Treatment
Bibliographic record
Abstract
Abstract Jessel et al. ( Behavior Analysis in Practice, 17 , 727–745, 2024) demonstrated that results from the performance-based, interview-informed synthesized contingency analysis (IISCA) had strong correspondence when compared to typical IISCA procedures and produced positive outcomes with resultant functional communication training procedures. On the basis of the assumption that functional analyses may include potentially adverse events insofar as they deliberately and repeatedly arrange conditions suspected to evoke dangerous behavior, Jessel and colleagues argued in favor of aligning functional analysis procedures with guidelines of trauma-informed care. We replicated and extended Jessel et al. ( Behavior Analysis in Practice, 17 , 727–745, 2024) by conducting a performance-based IISCA with three children with autism referred for behavioral services due to dangerous behavior and by evaluating a comprehensive skill-based treatment informed by the performance-based IISCA. The skill-based treatment resulted in the eventual elimination of dangerous behavior and the acquisition of multiple important skills, with caregivers implementing treatment sessions for two of the three participants. Assessment and intervention procedures and outcomes were socially validated by all participating families.
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 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".