Anger management interventions for adults with learning disabilities living in the community: a review of recent (2000–2010) evidence
Bibliographic record
Abstract
Accessible summary One in four adults with learning disabilities living in the community find it hard to manage their anger. This can make life hard for adults with learning disabilities and the people who support them. This study looks at research on helping adults with learning disabilities to manage their anger. More research needs to be carried out to find better ways of helping adults with learning disabilities to manage their anger. Summary Estimates suggest that around a quarter of adults with learning disabilities living in the community have difficulties controlling their anger. Angry or aggressive behaviour can have serious repercussions, including loss of residential or day placements, admission to hospital and reduced quality of life. In addition, the psychological well being of both paid and family carers can be adversely affected. A previous review of this area ( Whitaker 2001 ) suggested equivocal results for cognitive‐behavioural (CBT) approaches. The current study provides an update to this review, considering papers published in the last 10 years and expanding its scope to include a variety of therapeutic interventions. A systematic search of peer‐reviewed journals identified 14 relevant documents, the majority of which were group‐based and CBT in their approach. Overall, methodological weaknesses made it difficult to draw any firm conclusions about the effectiveness of the different approaches. Implications for clinical and research practice 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".