Contingent electric shock (SIBIS) and a conditioned punisher eliminate severe head banging in a preschool child
Bibliographic record
Abstract
Abstract We report a case in which a Self‐Injurious Behavior Inhibiting System (SIBIS) device and a conditioned punisher were utilized to decrease and maintain suppression of severe head hitting/banging in a preschool child. After an experimental evaluation conducted at the hospital, SIBIS was implemented at home. The originality of this particular SIBIS case study is that programmed and systematic effort at establishing conditioned punishment was included in the intervention. Results indicate that a zero‐level response was rapidly reached, and that the conditioned punisher (i.e. verbal prompt + movement towards the place where SIBIS was kept) was sufficient to maintain treatment effects. Continuous assessment after treatment and formal observation session at 7 months follow‐up revealed that SIBIS could be removed from the natural environment of the child while maintaining a therapeutic effect. These results were interpreted as the effects of the explicit pairing between the delivery of electric stimulations and previously neutral stimuli, which were initially ineffective to elicit any response, or to suppress SIB. Close and extended monitoring during and after treatment failed to reveal the presence of negative side effects associated with SIBIS, whereas a number of positive effects were observed. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".