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Cognitive Approaches to Schizophrenia: Theory and Therapy

2004· review· en· W2162583577 on OpenAlexaff
Aaron T. Beck, Neil A. Rector

Bibliographic record

VenueAnnual Review of Clinical Psychology · 2004
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyNeurocognitiveCognitionContext (archaeology)Dysfunctional familySchizophrenia (object-oriented programming)Cognitive psychologySet (abstract data type)ConceptualizationClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

A theoretical analysis of schizophrenia based on a cognitive model integrates the complex interaction of predisposing neurobiological, environmental, cognitive, and behavioral factors with the diverse symptomatology. The impaired integrative function of the brain, as well as the domain-specific cognitive deficits, increases the vulnerability to aversive life experiences, which lead to dysfunctional beliefs and behaviors. Symptoms of disorganization result not only from specific neurocognitive deficits but also from the relative paucity of resources available for maintaining a set, adhering to rules of communication, and inhibiting intrusion of inappropriate ideas. Delusions are analyzed in terms of the interplay between active cognitive biases, such as external attributions, and resource-sparing strategies such as jumping to conclusions. Similarly, the content of hallucinations and the delusions regarding their origin and characteristics may be understood in terms of biased information processing. The interaction of neurocognitive deficits, personality, and life events leads to the negative symptoms characterized by negative social and performance beliefs, low expectancies for pleasure and success, and a resource-sparing strategy to conserve limited psychological resources. The comprehensive conceptualization creates the context for targeted psychological treatments.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.415
GPT teacher head0.562
Teacher spread0.146 · 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 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

Citations186
Published2004
Admission routes1
Has abstractyes

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