Indicators of quality teaching in intensive behavioral intervention: a survey of parents and professionals
Bibliographic record
Abstract
Abstract Intensive Behavioral Intervention (IBI) is being used extensively with children with autism. It is widely accepted that a large quantity of IBI is necessary to maximize children's outcomes, but outcomes remain variable and one reason for this is likely related to the quality of intervention children are receiving. There is little empirical evidence regarding the nature and measurement of quality IBI. This paper presents results of a survey examining the views of parents and professionals about quality IBI and how it should be measured. Parents rated the importance of 11 IBI characteristics and professionals indicated whether these characteristics should be measured objectively or subjectively. All respondents selected three characteristics they thought most important and answered open‐ended questions about: additional quality indicators and IBI programming issues for which empirical evidence is needed. Parental ratings supported the importance of virtually all the suggested characteristics. Professional results emphasized the importance of objective measurement. The most frequently selected indicators of high quality teaching across groups were: creating generalization opportunities, administering reinforcers of the appropriate type, and using effective/appropriate behavior management strategies. There were interesting differences across groups and many valuable suggestions about additional indicators of quality and empirical questions of interest. Copyright © 2006 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".