Clients’ current presentation yields best prediction of criminal recidivism: Jointly modeling repeated assessments of risk and recidivism outcomes in a community sample of paroled New Zealanders.
Bibliographic record
Abstract
OBJECTIVE: Clinicians often rely on readily observable intermediate outcomes (e.g., symptoms) to assess the likelihood of events that occur outside of treatment (e.g., relapse). Similarly, those monitoring clients with histories of criminal involvement attempt to prevent adverse outcomes considered likely and intervene when symptoms/risk factors fluctuate. Our aim was to develop a stronger understanding of associations between evolving symptoms/risk factors and case outcomes, yielding clearer practice implications. METHOD: We used longitudinal, multiple reassessment risk data from 3,421 individuals paroled in New Zealand. We used joint modeling to test the association between individual trajectories of psychosocial risk factor scores, assessed using Dynamic Risk Assessment for Offender Re-entry, and recidivism (official records of parole violations or criminal charges resulting in reconviction). We examined whether recent clinically relevant features of risk presentation (e.g., current levels, recent rate of change) predicted recidivism better than the entirety of the risk assessment trajectory. RESULTS: Although each model demonstrated similar predictive validity, measures of model fit indicated that models using current trajectory features outperformed those using the entire assessment history to predict recidivism. CONCLUSIONS: Change in dynamic risk factors is consistently associated with recidivism outcomes. When using changeable factors to monitor clients' current risk for recidivism, practitioners should focus on current presentation rather than the entire assessment history, although differences in predictive discrimination are small. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".