Using Information From the Violence Risk Scale to Understand Different Patterns of Change
Bibliographic record
Abstract
Research rarely has shown that in-program change in correctional rehabilitation is related to long-term outcome (i.e., recidivism), and surprisingly little is known about what happens to progress after treatment, especially for "lifers" whose release may not be imminent. This study investigated patterns of treatment response for 35 life-sentenced treatment completers of an intensive cognitive-behavioral program for high-risk male violent prisoners. Using Violence Risk Scale (VRS) ratings at pre-treatment, post-treatment, and 6 to 12 months following the program, we found that prisoners' mean treatment response was positive both at program end and follow-up. However, a fine-grained analysis identified five distinct change patterns within the sample. Importantly, the direction and volume of in-program change did not necessarily predict post-program change, and the highest risk prisoners did not benefit as much as those at medium-high risk. The findings suggest that (a) a better understanding of the effects of treatment may be gained by examining change beyond the end of interventions, including a focus on the individual and contextual factors that promote and inhibit generalization and (b) more therapeutic attention may be warranted for monitoring treatment change to maximize conditions for continued gain beyond the end of the program.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".