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Record W2345627871 · doi:10.1002/bsl.2244

Factors influencing treatment team recommendations to review tribunals for forensic psychiatric patients

2016· article· en· W2345627871 on OpenAlexaffabout
Krystle Martin, Erica K. Martin

Bibliographic record

VenueBehavioral Sciences & the Law · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsTribunalForensic psychiatryPsychiatryHuman factors and ergonomicsForensic scienceSuicide preventionPoison controlInjury preventionMedicinePsychologyMedical emergencyPolitical scienceLaw

Abstract

fetched live from OpenAlex

It is the responsibility of forensic psychiatric hospitals to detain and treat patients, gradually reintegrating them into society; decisions to release patients must balance risk to the public with maintaining the least restrictive environment for patients. Little is known about the factors considered when making such decisions and whether these factors have been empirically linked to future risk of violence. The current study explores the factors predictive of forensic treatment teams' recommendations for patients under the care of the Ontario Review Board (ORB). Factors differ depending on level of security; decisions on medium secure units were influenced by the presence of active symptoms and patients' overall violence risk level and decisions made on minimum secure units were influenced by the number of critical incidents that occurred within the recommendation year. Understanding the factors used to make recommendations to the ORB tribunal helps treatment teams to reflect on their own decision-making practices. Furthermore, the results serve to inform us about factors that influence length of stay for forensic psychiatric patients. Copyright © 2016 John Wiley & Sons, Ltd.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.148
GPT teacher head0.423
Teacher spread0.275 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

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