MétaCan
Menu
Back to cohort
Record W2962213562 · doi:10.15173/ijrr.v2i1.3703

Implementation of Cognitive Behavioural Therapy for Psychosis (CBTp) in a forensic setting

2019· article· en· W2962213562 on OpenAlexaff
Kyrsten M. Grimes, Peter Sheridan

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2019
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSt. Joseph’s Healthcare HamiltonThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychosisContext (archaeology)Forensic psychiatryCognitionPsychologyPsychotherapistCognitive behaviour therapyCognitive therapyCognitive behavioral therapyPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

In this Letter to the Editor, the authors comment on the implementation of a cognitive behavioural therapy for psychosis program for individuals admitted to a forensic psychiatry program. They highlight their experience and how they adapted the sessions to fit the needs of the patient in this context.

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)0.007
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0340.032
Insufficient payload (model declined to judge)0.0020.002

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 head0.361
Teacher spread0.342 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Risk and RecoverySame topicSchizophrenia research and treatmentFrench-language works237,207