Measurement invariance of the Youth Self-Report across youth who have committed sexual and nonsexual offenses.
Bibliographic record
Abstract
Justice-involved youth experience high rates of mental health problems that require proper screening and assessment in order to effectively intervene. The Youth Self-Report (YSR) is a general psychopathology rating scale that measures several dimensions of psychopathology and is commonly used in clinical assessments, including with justice-involved youth. Yet, the underlying factor structure of the YSR has not been examined specifically in a sample of justice-involved youth. We examined the factor structure of the YSR using confirmatory factor analysis with a sample of 961 male youth involved with the justice system (12-18 years of age). Measurement invariance of the YSR was also examined across groups of youth who committed a sexual offence and those who committed a nonsexual offence. The eight-factor model presented with optimal fit to the data, consistent with previous research with nonjustice involved samples, and the model demonstrated strong measurement invariance across youth who committed both types of offenses (sexual and nonsexual). Youth who committed nonsexual offenses reported significantly higher degrees of rule-breaking behavior and lower degrees of social problems than youth who committed sexual offenses. The current findings provide strong psychometric evidence that supports the use of the YSR with justice-involved male youth. As such, clinicians and researchers can be confident in using the YSR as a mental health screening tool with male youth involved with the justice system who have committed various offenses. (PsycInfo Database Record (c) 2023 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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".