Exploring a new structured professional judgment measure (impulsivity measure related to violence) after an average follow‐up of 10 years: A study of Finnish offenders
Bibliographic record
Abstract
BACKGROUND: Identification of the risk factors underlying impulsivity related to violent acts is an essential component of risk assessment and management to reduce violent offending. AIMS: Our aim was to develop a clinically useful measure for assessing impulsivity related to violence. Our research questions were which items in the newly developed measure are associated with later violent recidivism and what is the measure's predictive validity? METHODS: A new scale, the impulsivity measure related to violence (IMP-V), was studied by completing the scale, blind to outcome, from information in the forensic psychiatric examination reports of 63 of a 1-year referral cohort of 181 Finnish offenders. Data on reoffending for up to 15 years after release were collected from official criminal records. RESULTS: The predictive accuracy of the IMP-V continuous ratings was 78% and for the categorical summary risk ratings 77%. Univariate analyses of categorical summary risk ratings of the risk factors revealed that, with two exceptions, each additional score on the IMP-V was associated with a significant increase in violence recidivism. CONCLUSIONS: These preliminary results indicate that the IMP-V is a promising decision-enhancing guide for assessing the risk of violence in impulsive people and that the measure is worth developing for use with impulsivity-prone offenders and forensic psychiatric patients. The IMP-V organises information on the nature of impulsivity in violence-prone persons and thus also creates opportunities for more effective risk management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".