How Should the Risk of Absconding Be Assessed? Existing Approaches within Forensic Mental Health Systems and Examination of a New Scale
Bibliographic record
Abstract
At present, there are few validated tools to assist clinicians in assessing absconding risk and formulating viable risk management plans. In this article, we review existing literature on instrument validity and reliability in relation to absconding among patients in forensic care. We examine the predictive validity of a new risk assessment scale for absconding, the Waypoint Elopement Risk Scale-Historical (WERS-H), and assess its incremental utility against a general violence risk assessment instrument (HCR-20 V3 ). Results from all active inpatients in our service ( N = 139) revealed 73 individuals who were responsible for 261 absconding events from 2014 to 2020, representing a similar event frequency from a previous census conducted in 2012, but also reflecting considerable annual fluctuations in rate. Confirming results of earlier studies, the presence of substance use and lengthy durations of forensic supervision emerged as key variables associated with absconding. The WERS-H was found to be a significant predictor of future absconding events (incident rate ratio = 1.21, 95% CI [1.07, 1.38], p = .002) and contributed incrementally over the HCR-20 V3 Historical scale, suggesting that the use of an absconding-specific risk tool may yield better predictive accuracy compared to assessment instruments in the domain of general violence or offending.
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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 | 0.050 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".