Assessing Protective Factors for Adolescent Offending: A Conceptually Informed Examination of the SAVRY and YLS/CMI
Bibliographic record
Abstract
Although the Structured Assessment of Violence Risk in Youth (SAVRY) and the Youth Level of Service/Case Management Inventory (YLS/CMI) are among the most widely used adolescent risk assessment tools, they conceptualize and measure strengths differently. As such, in this study, we compared the predictive validity of SAVRY Protective Total and YLS/CMI Strength Total, and tested conceptual models of how these measures operate (i.e., risk vs. protective effects, direct vs. buffering effects, causal models). Research assistants conducted 624 risk assessments with 156 youth on probation. They rated protective factors at baseline, and again at 3-, 6-, 9-, and 12-month follow-up periods. The SAVRY Protective Total and YLS/CMI Strength Total inversely predicted any charges in the subsequent 2 years (area under the curve scores = 0.61 and 0.60, respectively, p < .05). Furthermore, when adolescents’ protective total scores increased, their self-reported violence decreased, thus providing evidence that these factors might play a causally relevant role in reducing violence. However, protective factors did not provide incremental validity over risk factors. In addition, because these measures are brief and use a dichotomous rating system, they primarily captured deficits in protective factors (i.e., low scores). This suggests a need for more comprehensive measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".