Reduce, manage or cope: a review of strategies for training school staff to address challenging behaviours displayed by students with intellectual/developmental disabilities
Bibliographic record
Abstract
Members of a knowledge translation and exchange ( KTE ) research team assessed the training needs of the teaching staff at a school for individuals with intellectual/developmental disabilities ( IDD ). In response to this need, KTE researchers retrieved peer‐reviewed articles for training staff working with individuals with IDD who exhibit challenging behaviours. These articles were categorised according to the following training content: (1) interventions designed to reduce the frequency of challenging behaviours; (2) appropriate ways to manage challenging behaviours in the moment to promote safety for all parties and/or to terminate the ongoing behaviour; and/or (3) procedures or perspectives relevant to coping with or ameliorating the negative impacts of challenging behaviours on staff. We then examined the training methods (teaching strategies, training duration) involved in teaching the content and assessed the effectiveness of these programmes. Overall, we found that effective training programmes consisted of workshops, practica and feedback on specific skill performance. Some forms of brief training were effective for increasing staffs' knowledge/skills and reducing the frequency of challenging behaviour.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".