Effects of multiple interventions for reducing vocal stereotypy: Developing a sequential intervention model
Bibliographic record
Abstract
Despite the availability of several interventions designed to reduce engagement in vocal stereotypy, few studies have compared two or more interventions together. Consequently, practitioners have limited amount of data to make informed decisions on whether an intervention may be more suitable than another to begin treating vocal stereotypy. The purpose of the study was to address this limitation by examining the direct and collateral effects of multiple interventions in 12 individuals with autism and other developmental disabilities in order to guide the development of a sequential intervention model. Using single-case experimental designs, we conducted a series of four experiments which showed that (a) noncontingent music generally produced more desirable outcomes than differential reinforcement of alternative behavior, (b) differential reinforcement of other behavior reduced vocal stereotypy in two participants for whom noncontingent music had failed to do so, (c) the addition of simple prompting procedures may enhance the effects of the interventions, and (d) the effects of noncontingent music may persist during sessions with extended durations. Based on these results, we propose a sequential intervention model to facilitate the initial and subsequent selection of an intervention most likely to reduce vocal stereotypy while producing desired collateral outcomes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".