The Strengths Assessment Inventory–Youth Version: An evaluation of the psychometric properties with male and female justice-involved youth.
Bibliographic record
Abstract
Strengths constitute an important element of developmental assessments. It is consistent with evidence-based practice to use assessment tools that adequately measure a given construct and are appropriate for use with their targeted population. The Strengths Assessment Inventory-Youth Version (SAI-Y; Rawana & Brownlee, 2010)-a self-report measure of personal strengths, self-concept, and emotional functioning-was administered to 230 male and female adolescent offenders. Confirmatory factor analyses revealed that the SAI-Y's factor structure demonstrated an acceptable fit overall, while some factors fit the data well, and fewer factors displayed a questionable fit. A majority of scale scores were found to exhibit good reliability for both sexes, with three empirical scale scores demonstrating poor reliability. In addition, scores on the SAI-Y also achieved satisfactory convergent and divergent validity. Total strength scores were significantly correlated in the expected direction with most theoretically related measures of emotional and behavioral functioning (e.g., self-esteem, treatment readiness, antisocial attitudes). Lastly, moderate gender effects and small ethnicity differences in response patterns were found. This was the first validation study of the SAI-Y with a justice-involved sample and the results suggest it is an appropriate measure for use with both male and female justice-involved young persons in detention and in the community. (PsycINFO Database Record
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".