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Record W2986052454 · doi:10.15173/ijrr.v2i2.3920

Absconsion from forensic psychiatric institutions

2019· article· en· W2986052454 on OpenAlexaff
Danielle Campagnolo, Ivana Furimsky, Gary Chaimowitz

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsForensic psychiatryPsychiatryPopulationForensic scienceMental healthInclusion (mineral)Unit (ring theory)PsychologyInstitutionMedicineSocial psychologyLawPolitical science

Abstract

fetched live from OpenAlex

Background: Absconding from mental health units is referred to as a patient leaving without permission and can have significant consequences for the patient, family, community, and institution. The varying definitions of absconsion involve breaching security of an inpatient unit, accessing grounds or community without permission, gaining liberty during escorted leave or being absent for longer than permitted from authorized or trial leave. While considerable literature exists on absconsion from acute psychiatric units, there is a paucity of literature specific to forensic absconsions, despite inherent differences between patients and systems. Forensic patients are offenders who are found unfit to stand trial, or not criminally responsible on account of mental disorder. The literature indicates the absconding rate within the forensic population is expected to be low, based on the fact that the level of security in forensic units is higher than general psychiatric units. Despite the rates being considered low, the outcomes of absconding in this population can potentially be serious, thus the exploration of factors surrounding these incidents is essential. Purpose: To review the literature regarding absconsion from forensic psychiatric institutions. This review will identify potential risk factors and motivations of forensic patients that have absconded. Methods: Electronic database and hand searches were conducted to locate articles pertaining to absconding specific to forensic psychiatric institutions published from 1969-present. Search terms included “abscond”, “escape”, “AWOL”, “runaway”, “psychiatric inpatient”, “forensic institution”, & variants. All full-text articles meeting inclusion & exclusion criteria were appraised for qualitative themes, limitations, and assessed for risk of bias using appropriate CASP Checklists. The review is structured following the PRISMA checklist and framework. Results: A total of 19 articles meeting literature review criteria were identified. The majority of the articles were of retrospective case-control design (n=12). Three systematic reviews were found on absconsion that included analyses from both forensic and general psychiatric populations. Definitions for absconding were omitted or varied making comparisons between studies difficult. Much research compared demographic, static and dynamic factors. History of previous absconsion, scores on validated risk-of-violence assessment tools, substance-use disorder, acute mental state, and socio-environmental factors were consistently noted as risk-factors. Four distinct motivations for absconding emerged: goal-directed, frustration/boredom, symptomatic, and accidental. Overall, the literature suggested forensic absconsion was a rare event of short duration with low risk to the public and few re-offending incidents. Conclusions: There is a paucity of literature on forensic absconsions. A consistent definition of absconsion and use of standardized reporting protocols across forensic programs would be beneficial in order to be able to compare data on absconding events. Also, prospective studies should be undertaken to better understand the motivations and dynamic risk factors of forensic patients who have absconded and would help inform a forensic absconsion risk assessment protocol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.301
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2019
Admission routes1
Has abstractyes

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