Psychotherapy case formulation for patients with aggressive behaviours in clinical settings: an adaptation of the Multimodal Functional Model
Bibliographic record
Abstract
Aggressive behaviours are commonly observed in clinical settings, making it crucial to employ advanced assessment tools to accurately evaluate the likelihood of such behaviour. This article aims to present an assessment framework for developing a psychotherapy case formulation for patients with aggressive behaviours within the context of clinical setting. Recognising the disparities in the usage of aggression, as well as the comorbid nature of aggressive behaviours in patients with various psychopathologies, we propose an integrative framework that addresses these inconsistencies. The framework utilises the Hunter et al.’s (2008) Multimodal Functional Model as a foundation to which we integrated seven other models that are the I3 Model, the Algebra of Aggression model, the General Aggression Model, the Social Information Processing model, the Response Evaluation and Decision model, the Integrative Cognitive Model, and the nosographic model of mental disorders according to DSM-5. All these models were integrated into a comprehensive and expanded version of the Hunter et al.’s multimodal functional analysis worksheet, which combines the bio-psycho-social modalities of behaviour analysis in five factors that are instigation, vulnerability, reinforcement, habit strength, and inhibition. Additionally, case study example is provided to illustrate the development of a case formulation, which serves as a foundation for establishing therapeutic goals and implementing appropriate interventions. By incorporating a comprehensive understanding of aggression and utilising an adaptation of the multimodal functional analysis worksheet, this approach provides clinicians with a robust foundation for formulating effective therapeutic 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".