Evaluating the Effects of Picture Exchange Communication System® (PECS®) Mediator Training Via Telehealth Using Behavioural Skills Training and General Case Training
Bibliographic record
Abstract
The Picture Exchange Communication System® (PECS®) is often used by children diagnosed with autism spectrum disorder (ASD) as a means of functional communication. Although there is extensive research indicating that PECS is an evidence-based intervention for children with ASD (e.g., Wong et al., 2015), little is known about how best to train parents to support their child’s PECS use. Of those studies that do explore parent training approaches, few measure the caregiver’s fidelity implementing PECS or explore whether parents generalize or maintain skills post-training. Similarly, little is known about how to train parents to implement PECS via telehealth. The purpose of the current study was to bridge the gap between PECS and telehealth research and to explore strategies to help parents support their child’s PECS use at home. One father-mother dyad was recruited. The father was the primary training recipient (i.e., parent trainee). The mother participated in training sessions as the role play partner (i.e., surrogate parent). Researchers used behavioural skills training (BST) to teach target PECS skills and applied strategies of general case training (GCT) to actively program for generalized behaviour change. A multiple baseline design across skills was used to monitor the father’s fidelity during mediator training sessions and a multiple probe design was embedded to monitor both the father’s and mother’s fidelity in the natural environment with their child. Results demonstrated that the parent trainee acquired PECS skills within the training setting. However, parents did not reliably demonstrate all of the PECS skills in the generalization setting during follow-up.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".