The influence of negative and affective symptoms on anhedonia self-report in schizophrenia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".