Is a 7-Item Combination from the YLS/CMI an Effective Screening Strategy for Risk to Reoffend? Findings from a Cross-National Study
Bibliographic record
Abstract
Several brief screening measures for youth risk to reoffend have been developed; however, these measures have been tested primarily in high-income English-speaking countries and their predictive validity is limited. A recent study proposed a screening strategy using a combination of seven items from the Youth Level of Service/Case Management Inventory (YLS/CMI). Predictive validity for this strategy was better than that reported in studies of previously developed screening tools. In the current study, the predictive validity of this strategy was examined across samples of justice-involved youth from two countries: Canada ( N = 196) and Portugal ( N = 2,348). The full version of the YLS/CMI was completed and recidivism data were collected over a 2-year period. Results support the predictive validity of this strategy, with area under the curve (AUC) values (.69–.74) very similar to those found in the full version in each country, both in the full samples, and for both genders.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".