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The influence of negative and affective symptoms on anhedonia self-report in schizophrenia

2020· article· en· W3002609468 on OpenAlexaboutno aff
Isaac Jarratt Barnham, Youssuf Saleh, Masud Husain, Brian Kirkpatrick, Emilio Fernández-Egea

Bibliographic record

VenueComprehensive Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersNIHR Cambridge Biomedical Research CentreNational Institute for Health and Care ResearchWellcome Trust
KeywordsAnhedoniaSchizophrenia (object-oriented programming)Scale for the Assessment of Negative SymptomsDepression (economics)PsychiatryCognitionPsychologyClinical psychologyNegative symptomPleasurePsychosisPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Anhedonia, a symptom prevalent in schizophrenia patients, is thought to arise either within negative symptomatology or from secondary sources, such as depression. The common co-occurrence of these diseases complicates the assessment of anhedonia in schizophrenia. METHOD: In a sample of 40 outpatients with chronic schizophrenia, we explored both the validity of the Snaith-Hamilton Pleasure Scale (SHAPS) self-report for anhedonia assessment and those factors influenced its scoring. We assessed negative symptoms using the Brief Negative Symptom Scale (BNSS), depression symptoms using the Calgary Depression Scale for Schizophrenia (CDSS) and cognitive impairment using the Brief Assessment of Cognition in Schizophrenia (BACS), before exploring associations between these scales. RESULTS: The SHAPS was validated for use in schizophrenia. SHAPS scores were not associated with negative symptoms or cognitive impairment, but were linked to a single Depression symptom: Hopelessness (r = 0.52, p < 0.001). CONCLUSIONS: SHAPS scores, therefore, appear to only reflect anticipatory anhedonia arising from the affective domain. We advocate the development of multi-faceted self-report measures to more holistically assess anhedonia in schizophrenia.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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