Unraveling the Complex Relationships Between Anxiety, Depression, and Quality of Life in Schizophrenia: A Network Analysis Study
Bibliographic record
Abstract
Abstract Background Schizophrenia, a debilitating mental disorder, impacts cognitive, behavioral, and emotional functions. Co-occurring anxiety and depression worsen its complexity and diminish patients' quality of life. This study uses a network analysis approach to explore the relationships among anxiety, depression, and quality of life in hospitalized schizophrenia patients. Methods Cross-sectional study on 1328 inpatients with schizophrenia. Data included demographics, clinical details, and self-reported depression (HAMD-17), anxiety (HAMA-14), and quality of life (SQLS-R4). Network analysis employed Gaussian graphical models and Lasso for sparse network estimation. Results The analysis revealed hopelessness as the central node in quality of life, emphasizing its role in overall well-being. Somatic anxiety emerged as the central node in depression, highlighting the need to address somatic symptoms. Sleep disturbances were prominent central nodes in anxiety, indicating the need for targeted interventions. Discussion This study provides valuable insights into the relationships between anxiety, depression, and quality of life in inpatient schizophrenia populations. Addressing key symptoms such as hopelessness, somatic anxiety, and sleep disturbances can significantly improve overall well-being. Integrated interventions for anxiety and depression, along with comprehensive strategies addressing psychosocial factors, are crucial for optimizing therapeutic outcomes and enhancing quality of life in individuals with 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".