Teaching Community Service Providers to Support Caregiver PECS Use: An Evaluation of the PECSperts Facilitator Training Model
Bibliographic record
Abstract
Research examining how to train facilitators to implement the Picture Exchange Communication System (PECS) with caregivers and children is scarce. This research gap presents a barrier to offering PECS as a community service. Currently, there is some empirical research supporting the effectiveness of behaviour skills training (BST) and pyramidal training to train facilitators to implement behavioural interventions. Building on this foundation, the present study used a quasi-experimental, non-randomized group design to explore the effectiveness of a manualized BST training model (i.e., the PECSperts Facilitator Training Package) within a pyramidal training approach. Sixteen community facilitators participated in the study. Participants who immediately received training demonstrated a significant increase in treatment integrity from pre- to post-training assessments. These results maintained during the five week follow up period and generalized to the participants’ clinical practice with families in the community. Participants who did not immediately receive training, did not demonstrate an increase in treatment integrity from pre- to post-training assessments. Later, the training procedures were replicated with the participants who did not initially receive training. After participation in the PECSperts Facilitator Training, these participants’ treatment integrity scores also significantly increased. Participants rated the training positively on a social validity questionnaire. The results provide preliminary evidence for the use of the PECSperts Facilitator Training as an effective training model for training facilitators to teach caregivers to implement PECS with their autistic children. Results of this study have implications for increasing community capacity and access to facilitator PECS training.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".