The Dynamic Prediction of Antisocial Behavior Among Forensic Psychiatric Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Staff ratings of 595 supervised forensic psychiatric patients on the Proximal Risk Factor Scale and the Problem Identification Checklist were completed monthly for an average of 33 months. During the follow-up, there were 265 incidents, 86 of which were violent. The average ratings, excluding those from the index month, differentiated patients who had incidents from those who did not. As well, the average ratings distinguished between individuals with and without incidents of a violent or sexual nature. There were significant increases in staff ratings in the months preceding the index incident month. Within-patient analyses showed that changes in dynamic risk scales comprising the best items for predicting incidents of any kind and violent or sexual incidents were strongly related to their respective outcomes and were significantly related to outcome in an independent sample. Changes in monthly staff ratings predict the imminent occurrence of antisocial and violent behaviors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it