MétaCan
Menu
Back to cohort
Record W2119983937 · doi:10.1177/002580240204200410

Conditionally Discharged Restricted Patients and the Need for Long-Term Medium Security

2002· article· en· W2119983937 on OpenAlexaboutno aff
Sharon Riordan, Helen Smith, Martin Humphreys

Bibliographic record

VenueMedicine Science and the Law · 2002
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsnot available
Fundersnot available
KeywordsMental Health ActPsychiatryContext (archaeology)Mental healthQuarter (Canadian coin)Mental illnessHarmMedicineSentenceMentally illMaximum securityRevolving doorUnit (ring theory)Criminal recordPsychologyCriminologyPrisonLawSocial psychologyHistory

Abstract

fetched live from OpenAlex

In the context of a larger investigation of follow-up of a specific group of mentally disordered individuals, the study described here examined the characteristics of all 55 people conditionally discharged for the first time from a medium secure unit in the West Midlands over a 13-year period. A retrospective case note analysis was undertaken. The findings illustrate that these patients are a distinct group. They were mainly single men who had committed a grave offence. The majority had an extensive criminological history with early onset of offending and chronic mental illness. Fifty percent of those with a criminal record had received at least one custodial sentence prior to the index offence. Co-morbid substance misuse was common, as was a history of self-harm. There were high levels of previous contact with psychiatric services and compulsory in-patient treatment. Most were detained under the Mental Health Act category of mental illness. A quarter had been transferred from a special hospital prior to conditional discharge into the community. The characteristics of this sample demonstrate clearly the need for the provision of long-term medium secure facilities and allied services.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.019
GPT teacher head0.317
Teacher spread0.298 · 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.

Study designTheoretical or conceptual
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

Citations7
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueMedicine Science and the LawSame topicPsychiatric care and mental health servicesFrench-language works237,207