<p>Negative and positive self-thoughts predict subjective quality of life in people with schizophrenia</p>
Bibliographic record
Abstract
PURPOSE: Recently, cognitive variables such as negative and positive self-belief and thoughts have attracted much attention because they are associated with functional outcomes and quality of life (QOL). However, it is unclear how cognitive variables affect subjective and objective QOL. This study aimed to investigate the relationship of negative and positive self-belief and thoughts with subjective and objective QOL. PARTICIPANTS AND METHODS: Thirty-six people with schizophrenia participated in this study. Subjective and objective QOL were assessed with the Schizophrenia Quality of Life Scale (SQLS) and Quality of Life Scale (QLS), respectively. Neurocognitive function was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS). Clinical symptoms were assessed with the Positive and Negative Syndrome Scale and Calgary Depression Scale for Schizophrenia. Side effects were assessed with the Drug-induced Extrapyramidal Symptoms Scale (DIEPSS). Negative and positive self-belief and thoughts were assessed with the Defeatist Performance Belief Scale and Automatic Thoughts Questionnaire-Revised. A generalized linear model was tested, with subjective and objective QOL as the response variable and symptoms, neurocognitive function, and cognitive variables that were significantly correlated with subjective and objective QOL as explanatory variables. RESULTS: In the schizophrenia group, the common objects score on the QLS was predicted by the composite BACS score, and the total QLS score was predicted by the DIEPSS score. Motivation and Energy, Psychosocial, and Symptoms and Side effects scores on the SQLS were predicted by depression and by negative automatic thought (NAT) and positive automatic thought (PAT). CONCLUSION: Our results indicated that key targets for improving objective and subjective QOL in people with schizophrenia are side effects, neurocognitive function, depression, and NAT and PAT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".