Training in Trauma-Informed Positive Behaviour Support for Direct Support Professionals of Adults with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
People with disabilities are vulnerable to experiencing trauma due to a complex interaction of systemic and individual factors (McGilvery, 2018), urging for supports that promote safety and control, such as Trauma-Informed Care (TIC) and Positive Behaviour Support (PBS). Behavioural Skills Training (BST) is a competency-based training procedure used to teach a wide selection of professionals work-related skills (Gormley et al., 2019). This study evaluated a multicomponent training package consisting of didactic and activity-based training plus BST implemented with 12 direct support professionals recruited from a community-based organization supporting adults with intellectual and developmental disabilities. A pre-post- follow-up design was used to determine the effectiveness of didactic and activity-based training on knowledge and application of TIC and PBS. BST was used to increase skills related to trauma-informed PBS for a subset of six participants using a multiple probe within-participants single-case experimental design (Ledford & Gast, 2018). The three skills trained were providing opportunities for choice, multiple stimulus without replacement, and paired item preference assessments. Training resulted in increased knowledge and application of PBS and TIC on post- tests and at follow-up. BST was effective in training all skills to mastery, while maintenance and generalization were evident for most skills four to five weeks after training. Implications of the findings, strengths and limitations of the study, and future steps are discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".