MétaCan
Menu
Back to cohort
Record W2535908825 · doi:10.1016/j.jflm.2016.10.007

Pathways through the criminal justice system for prisoners with acute and serious mental illness

2016· article· en· W2535908825 on OpenAlexfundno aff
Karen Slade, Chiara Samele, Lucia Valmaggia, Andrew Forrester

Bibliographic record

VenueJournal of Forensic and Legal Medicine · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
FundersSt George's University Hospitals NHS Foundation TrustTrent UniversityNottingham Trent University
KeywordsPrisonImprisonmentCriminal justiceMental healthPsychiatryMental illnessMedicineSuicide preventionPoison controlPsychologyCriminologyMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate pathways through the criminal justice system for 63 prisoners under the care of prison mental health services. RESULTS: A small number (3%) were acutely mentally ill at prison reception, which may reflect the successful operation of liaison and diversion services at earlier stages in the pathway. However, a third (33%) went onto display acute symptoms at later stages. Cases displaying suicide risk at arrest, with a history of in-patient care, were at increased risk of acute deterioration in the first weeks of imprisonment, with a general absence of health assessments for these cases prior to their imprisonment. Inconsistencies in the transfer of mental health information to health files may result in at-risk cases being overlooked, and a lack of standardisation at the court stage results in difficulties determining onward service provision and outcomes. CONCLUSIONS: Greater consistency in access to pre-prison health services in the criminal justice system is needed, especially for those with preexisting vulnerabilities, and it may have a role in preventing subsequent deterioration. A single system for health information flow across the whole pathway would be beneficial.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.304
Teacher spread0.280 · 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 designNot applicable
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

Citations12
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Forensic and Legal MedicineSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207