Does reassessment of risk improve predictions? A framework and examination of the SAVRY and YLS/CMI.
Bibliographic record
Abstract
Although experts recommend regularly reassessing adolescents' risk for violence, it is unclear whether reassessment improves predictions. Thus, in this prospective study, the authors tested 3 hypotheses as to why reassessment might improve predictions, namely the shelf-life, dynamic change, and familiarity hypotheses. Research assistants (RAs) rated youth on the Structured Assessment of Violence Risk in Youth (SAVRY) and the Youth Level of Service/Case Management Inventory (YLS/CMI) every 3 months over a 1-year period, conducting 624 risk assessments with 156 youth on probation. The authors then examined charges for violence and any offense over a 2-year follow-up period, and youths' self-reports of reoffending. Contrary to the shelf-life hypothesis, predictions did not decline or expire over time. Instead, time-dependent area under the curve scores remained consistent across the follow-up period. Contrary to the dynamic change hypothesis, changes in youth's risk total scores, compared to what is average for that youth, did not predict changes in reoffending. Finally, contrary to the familiarity hypothesis, reassessments were no more predictive than initial assessments, despite RAs' increased familiarity with youth. Before drawing conclusions, researchers should evaluate the extent to which youth receiving the usual probation services show meaningful short-term changes in risk and, if so, whether risk assessment tools are sensitive to these changes. (PsycINFO Database Record
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".