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Record W4311951527 · doi:10.1080/14789949.2022.2157312

Disrobing behaviour in a forensic inpatient, a case report

2022· article· en· W4311951527 on OpenAlexaff
Juliette Dupré, Ipsita Ray, Shaheen Darani

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyPsychiatrySexual abuseDistressMental illnessPsychological interventionPopulationClinical psychologyPoison controlInjury preventionMedicineMental healthMedical emergency

Abstract

fetched live from OpenAlex

Disrobing is a behaviour with serious implications for sexual safety. It is a challenge to manage in the inpatient environment and a barrier to discharge to the community. This paper presents an overview of the literature on disrobing – distinct from exhibitionistic disorder – and primarily described in the developmental disabilities’ literature, and with respect to affective psychoses and epileptic post-ictal states. A case of disrobing in a forensic inpatient is outlined, which does not easily fit within the previously described explanatory or diagnostic paradigms for this behaviour. Behavioural and psychological analyses are reported, leading to a formulation of disrobing in this case as a complexly derived behaviour in a patient with chronic psychotic illness and a history of childhood sexual abuse. The importance of transdiagnostic thinking in patients with such complex histories is reviewed with attention to the intersection of deficits in distress tolerance from chronic psychotic illness and childhood sexual trauma. This case highlights the need for more research into trauma-informed interventions suitable for the serious mental illness (SMI) population.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.022
GPT teacher head0.350
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2022
Admission routes1
Has abstractyes

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