Risk Assessment: Patient and Detainee
Bibliographic record
Abstract
Abstract Risk assessment is required in a variety of legal and clinical settings. Nevertheless, there is still substantial disagreement about its scientific validity and how best to implement it into practice, which has led to extensive empirical research into its tenability. Violence risk factors can be grouped into four categories: (i) dispositional, reflecting personal traits and tendencies to act in a particular way; (ii) historical, indexing events in the past that may predispose a person to become violent; (iii) contextual, referring to elements in the current environment that are conducive to violence; and (iv) clinical, including features of mental illness, level of functioning, and substance abuse. Over the years the primary goal of risk assessment has shifted from simple determination of who would become violent in the future, to risk management, which allows for reduction of violence. There are two primary approaches to risk assessment: unstructured, based solely on clinical experience and professional opinion, and structured, which requires professionals to follow specific rules of identification and definition of risk factors. Structured risk assessment in turn includes two approaches: (i) actuarial, which relies on an algorithm to combine risk factors, which enter the final decision regarding future violence; and (ii) structured professional judgments (SPJs), which allow the decision maker to consider how nomothetically supported risk factors are relevant to a given individual, and what management strategies would mitigate risk. Commentary and research addressing the strengths and weaknesses of these approaches is provided.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".