Evaluation of A Self-Instructional Package for Teaching Parents to Conduct Discrete-Trials Teaching with Children with Autism
Bibliographic record
Abstract
Discrete-Trials Teaching (DTT) is commonly used in early intensive behavioral intervention for teaching children with ASD.DTT involves a teacher presenting an antecedent to the child, waiting for the child's response, and then providing a consequence for that response (either a reinforcer for a correct response or non-interaction for an incorrect response).The aim of this study was to assess the effectiveness of the Fazzio and Martin DTT self-instructional manual plus video (2011) with mothers of children with ASD as the participants.A multiple-baseline design across a pair of participants was used, and replicated across a second pair.During the baseline assessment, a participant was asked to teach three tasks (pointing-to-named pictures, identity matching and imitation) to a confederate role-playing a child with ASD.The participant was given one-page summaries for each teaching task and no additional information.Once baseline data was collected, the participant had the opportunity to study the self-instructional package, after which she conducted a post-treatment DTT session with the confederate.If she did not achieve mastery (set at 80%) in post-treatment assessment she was provided with a feedback session on her DTT performance, and then conducted an additional DTT session with the confederate.Three of the participants were available to conduct a generalization session with her child.The treatment package was very effective for training two of the mothers with children with ASD to implement DTT, and somewhat effective for the other two mothers who required a feedback session.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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".