Evaluating a self-instructional package on discrete-trials teaching with parents of children with Autism
Bibliographic record
Abstract
The purpose of this research was to evaluate a self-instructional package (Fazzio & Martin, 2007) to train parents of children with autism to conduct discrete-trials teaching (DTT). In Study 1, I investigated the effectiveness of a self-instructional manual and a self-instructional video for teaching five parents of children with autism to correctly apply DTT to teach three tasks to a confederate who role-played a child with autism. For three of the parents I also evaluated their ability to apply DTT to their children with autism. Following an average of 4.76 hours of training, the package produced a strong effect with three parents and a weak effect with two parents. In Study 2, I investigated the effectiveness of the self-instructional manual combined with role-playing and feedback, plus the self-instructional video, for teaching an additional five parents of children with autism to apply DTT to a confederate and to their children. Following an average of 4.68 hours of training, all five parents demonstrated large, clinically significant gains in their performance of DTT, both with a confederate as well as with their own child, with a minimal investment of one-on-one instructor time. The treatment procedures in both experiments were very well received by the parent participants. These results suggest that the training package in Experiment 2 has considerable potential as an effective, efficient and acceptable method of training parents of children with autism to apply DTT.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".