An Evaluation of Staff Training in Positive Behaviour Support
Bibliographic record
Abstract
AIMS: Challenging behaviour is common for many people with learning disabilities and has a negative impact on the lives of these individuals. It is linked to decreased levels of support from staff, reduced opportunities for inclusion in the community, use of restrictive interven-tions, and placement breakdown. Equipping staff with the necessary knowledge, skills and experience to support people with challenging behaviour in a positive, respectful and effec-tive way has proved a challenge for care agencies. Positive Behaviour Support (PBS) has been shown to be effective in minimising challenging behaviour. The aim of this study was to evaluate the impact of training managers of social care services in PBS. METHOD: A longitudinal training programme in PBS was delivered to 50 managers of community-based services for people with learning disabilities and challenging behaviour. The training pro-gramme lasted a year; data were collected pre and post training, and at 6 month follow-up. A non-randomised control group design was used. RESULTS: Data demonstrated significant reduction in challenging behaviour which was sustained over time. However, there was no change in quality of life for service users, and very limited changes in staff support to ser-vice users. CONCLUSION: This study has demonstrated that training managers in PBS can have a positive impact on challenging behaviour in people with learning disabilities. There are a number of aspects to the results which are unexpected and these are discussed with ref-erence to the relevant literature. Tizard
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".