A compendium of risk and needs tools for assessing male youths at-risk to and/or who have engaged in sexually abusive behaviors
Bibliographic record
Abstract
Using a standardized, validated risk assessment tool is an integral part of risk management and should be employed to evaluate a youth who is at risk to and/or has engaged in sexually abusive behaviors. Risk and needs tools are needed to inform critical decisions about the allocation of services and the areas that should be targeted in treatment and supervision. Although practitioners have a number of published tools to their avail, it is often less practical to discover the type of tool, where to access the tool, information regarding its psychometric properties, and how to access relevant training. This paper offers a brief compendium of youth-applied risk tools specific to male youths who are at risk to and/or who have engaged in sexually abusive behaviors; specifically, a description of the tool and its psychometric properties, along with where practitioners may access these tools and any relevant training in using these tools, are summarized. In light of the challenges that exist when assessing risk among youths, caveats and considerations are also explored.
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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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 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".