Clinical Overrides With the YLS/CMI: Predictive Validity and Associated Factors
Bibliographic record
Abstract
This study explores the use of clinical override with the Youth Level of Service/Case Management Inventory (YLS/CMI), including implications for predictive validity as well as the factors associated with this practice. The sample included 597 justice-involved youth from a metropolitan region in Québec, Canada. The clinical override was used in 32.5% of cases, usually to increase risk levels (30.3% of cases). As found in previous studies, clinical override did not increase the predictive validity of the YLS/CMI. Upward and downward clinical overrides were differently linked to the sociodemographic characteristics and criminal history of the youths in the sample. Criminal History, Peer Relations, Personality/Behavior, and Attitudes/Orientation risk/need domains were positively associated with upward override while Family Circumstances/Parenting, Personality/Behavior, and Attitudes/Orientation risk/need domains were negatively associated with downward override. These results are discussed in relation to the impact clinical override can have on the case management and interventions provided to justice-involved youth.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".