Strengths-Based Assessments for Use with Forensic Populations: A Critical Review
Bibliographic record
Abstract
With the emergence of positive psychology, correctional researchers have begun to infuse assessment practices with the consideration of strengths in an effort to understand why some high-risk individuals have the ability to avoid engaging in delinquent or criminal actions. Accordingly, the current article reviews eight offender assessment tools that either (1) incorporate strengths in addition to the traditionally measured set of risks/needs, or (2) were specifically designed as strength assessment protocols to be used alongside risk/needs tools. Although evidence is mixed, there is some preliminary support for the quantitative inclusion of strengths in risk assessment with the objective of improving predictive accuracy and providing valuable case planning information. Given definitional discrepancies in the literature with respect to how strengths are measured and conceptualized, further research is required to elucidate the specific manner in which strengths interact with established risk factors across various offender populations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".