Predicting Treatment Attrition Among Seriously Violent Offenders
Bibliographic record
Abstract
Treatment completion by violent offenders results in fewer victims and less violence in society. As researchers and members of society, we have a compelling interest in finding ways to keep violent male offenders in effective treatment programs. This study examines file-rated predictors of treatment attrition from an institutionally based program for persistently violent offenders. Each of the three prediction models of institutionally based treatment attrition included the predictors of motivation for assistance and prior treatment dosage: (a) the past criminal behavior model, (b) the recent antisocial behavior model, and (c) the non-antisocial instability model. Recent antisocial behavior did not improve the prediction of treatment attrition over the past criminal behavior model. Motivation for assistance did not make a contribution in the recent antisocial behavior or the non-antisocial instability models while prior treatment dosage consistently contributed to the prediction of attrition across the models. Recent non-antisocial behavior is important to offender treatment attrition.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".