Errorless Compliance Training to Reduce Extreme Conduct Problems and Intrusive Control Strategies in Home and School Settings
Bibliographic record
Abstract
The present case study involved intervention for a 7-year-old boy with a history of extreme aggression and noncompliance. Given the severity of his behavioral difficulties, his parents used highly coercive consequences, including physical restraint, which significantly compromised the parent—child relationship. The authors used errorless compliance training, a success-based, noncoercive intervention strategy to assist the mother in obtaining child cooperation without need for physical intervention. Although initial intervention attempts were ineffective because of the poor quality of the mother—child bond, systematic adjustments to the intervention eventually produced substantial improvements in child compliance in the home. Concurrent use of the intervention in the child’s classroom led to meaningful gains in classroom compliance. Anecdotal reports from the mother after intervention suggested widespread improvements in prosocial behavior and the parent—child relationship. The study findings provided support for use of errorless compliance training as a home and school-based alternative to interventions that require punitive or coercive consequences to suppress severe antisocial behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".