Impaired Insight into Illness is Unrelated to Subjective Happiness, Success, and Life Satisfaction in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Impaired insight into illness is a common feature of schizophrenia. Improved insight is associated with better treatment adherence and clinical outcomes. At the same time, improving insight has been suggested to increase depressive symptoms and diminish quality of life. The aim of this study was to examine the associations between impaired insight and degree of subjective happiness, perceived level of success, and life satisfaction in patients with schizophrenia spectrum disorders. METHODS: A total of 108 participants with schizophrenia or schizoaffective disorder were included. Data for this study were obtained from our group's previous investigation that examined the relationship between impaired insight and visuospatial attention. Insight into illness was measured by the VAGUS scale, which assesses general illness awareness, accurate symptom attribution, awareness of the need for treatment, and awareness of the negative consequences attributable to the illness. RESULTS: Our results revealed no association among the VAGUS average and subscale scores and degree of subjective happiness, perceived level of success, and life satisfaction. CONCLUSIONS: Our study suggests that insight into illness is not related to subjective happiness, life satisfaction, or perceived level of success in patients with schizophrenia, which is in contrast to previous reports that demonstrate an association between insight into illness and depression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".