The role of personality in aggressive behaviour among individuals with intellectual disabilities
Bibliographic record
Abstract
BACKGROUND: Aggressive behaviour is associated with certain personality traits in both the general population and among individuals with mental health problems, but little attention has been paid to the relationship between aggressive behaviour and personality among individuals with intellectual disabilities (ID). The aim of this study was to circumscribe personality profiles associated with aggressive behaviour among individuals with ID. METHOD: In this cross-sectional study of 296 adults with mild or moderate ID, information on mental health, personality and aggressive behaviour was gathered through structured interviews with the ID participants and their case manager, and a review of client files. RESULTS: The results of the Reiss Profile were submitted to hierarchical cluster analysis method. Subsequently, the distribution of aggressive behaviour, sociodemographic characteristics and clinical characteristics across personality profiles was analysed. The analyses yielded seven distinct personality profiles in relation to patterns of aggressive behaviour: Pacifists, Socials, Confidents, Altruists, Conformists, Emotionals and Asocials. CONCLUSION: The identification of distinct personality profiles sheds light on the risk factors for aggressive behaviour, and suggests new approaches to improving diagnostic and intervention strategies.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".