Absconsion in forensic psychiatric services: a systematic review of literature
Bibliographic record
Abstract
While serious concerns are often raised when patients abscond or leave unauthorized from psychiatric services, there is limited knowledge about absconsion in forensic psychiatric services. Following the preferred reporting items for systematic reviews and meta-analyses guideline, we searched Medline/PubMed, PsycINFO, EMBASE, CINAHL, Scopus, and Web of Science through May 2020 for eligible reports on absconsion in forensic patients with no language limits. The search string combined terms for absconsion, forensic patients, and psychiatry in various permutations. This was supplemented by snowball searching for additional studies. Of the 565 articles screened, 25 eligible studies, including two interventional, seven cross-sectional, and 16 case-controlled studies spanning five decades were included. Absconsion and re-absconsion rates ranged from 0.2% to 54.4% and 15% to 71%, respectively, albeit higher rates trended with less secure psychiatric units. Previous absconsion, aggression, substance use, high Historical Clinical Risk Management-20 score, anti-sociality, psychiatric symptoms, sexual offending, and poor treatment adherence were the factors reported with a degree of predictive value for absconsion. However, the construct of absconsion was heterogeneous in the included studies and the quality of evidence on the predictors of absconsion was limited. Serious risky behaviors including re-offending, violence, self-harm, suicide, rape, and manslaughter were perpetrated by patients during unauthorized leave. Nevertheless, the rates of re-offending were generally low in the included studies (highest recidivism rate = 0.11). There is need for standardized assessment and documentation of absconsion to improve risk analysis and management. Furthermore, it is necessary to develop a structured guideline for defining absconsion, and to create a protocol that operationalizes all absconsion-related behaviors/events to promote reliable assessment and comparative analysis in future studies.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".