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Record W2193640954 · doi:10.3371/csrp.meba.022015

Psychosocial Approaches in the Treatment of Psychosis: Cognitive Behavior Therapy for Psychosis (CBTp) and Metacognitive Training (MCT)

2015· review· en· W2193640954 on OpenAlexaff
Mahesh Menon, Ryan Balzan, Katy Margaret Harper, Devavrata Kumar, Devon Andersen, Steffen Moritz, Todd S. Woodward

Bibliographic record

VenueClinical Schizophrenia & Related Psychoses · 2015
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychosisPsychosocialPsychologyPsychotherapistSchizophrenia (object-oriented programming)CognitionClinical psychologyCognitive behavioral therapyIntegrative psychotherapyAntipsychoticMetacognitionCognitive therapyPsychiatry

Abstract

fetched live from OpenAlex

Although antipsychotic medication has been the most widely used and efficacious treatment in ameliorating the symptoms of psychosis, there has been a growing realization that pharmacological treatment has limitations. A significant minority of individuals continue to show "treatment-resistant" symptoms and significant relapse risk, while others show symptom reduction without the corresponding improvement in social and role functioning. Psychotherapy, in combination with medication, can help with symptom reduction, as well as improve functioning and quality of life. In this paper, we focus on two modalities of psychotherapy which have been shown to improve symptomatology and functioning in individuals with psychosis: Cognitive Behavior Therapy for psychosis (CBTp) and Metacognitive Training (MCT). Both treatment approaches focus on increasing the individuals' understanding of the psychological mechanisms associated with delusions and hallucinations, and helping them develop strategies to improve reality testing and belief evaluation. We aim to provide an overview of both treatments, examining not only the theoretical mechanisms and efficacy of each approach, but also the common therapeutic components they share.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.397
GPT teacher head0.496
Teacher spread0.098 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations16
Published2015
Admission routes1
Has abstractyes

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