Effects of a mindfulness training program on behaviour intervention procedural fidelity
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a lifelong developmental disability that is characterized by challenges with social communication and social interaction as well as repetitive behaviours, interests, or activities (American Psychiatric Association, 2013). An effective and well documented instructional approach for children with ASD is Discrete Trials Teaching (DTT). Since procedural fidelity is related to client outcomes (e.g., Noell, Gresham, & Gansle, 2002; Rhymer, Evans-Hampton, McCurdy, & Watson, 2002; Wilder, Atwell, & Wine, 2006), it is important to evaluate the accuracy with which staff conduct DTT sessions. Mindfulness training has been applied to improve attention, reflection, and skillful responding (Bishop et al., 2004) and has been proposed as a method to improve patient safety through adherence to prescribed procedures (Epstein, 1999; Pezzolesi, Ghaleb, Kostrzewski, & Dhillon, 2013). This study measured the effect of a brief mindfulness training program on procedural fidelity of staff conducting DTT with children with ASD. Three autism tutors from St.Amant Autism Programs participated. In a multiple-baseline design across participants, the principal investigator and a trained research assistant directly observed the accuracy with which tutors delivered prescribed DTT steps. DTT procedural fidelity increased during training for all participants. Specifically, during the baseline phase, procedural fidelity for Participants 1, 2, and 3 averaged 81%, 69%, and 70%, respectively. During the training phase, procedural fidelity increased to 86%, 83%, and 82%, respectively. Procedural fidelity was maintained at a high level for 2 of the 3 participants who were available at 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".