Predictors of quality of life among individuals with schizophrenia
Bibliographic record
Abstract
PURPOSE: The study reported here aimed to evaluate both biological and psychosocial factors as predictors for quality of life as well as to examine the associations between the factors and quality of life in individuals with schizophrenia. METHODS: Eighty individuals with schizophrenia were recruited to the study. The Thai version of the World Health Organization Quality of Life-BREF was utilized to measure the quality of life. The five Marder subscales of the Positive and Negative Syndrome Scale were applied. Other tools for measurement included the Calgary Depression Scale for Schizophrenia and six social support deficits (SSDs). Pearson/Spearman correlation coefficients and the independent t-test were used for the statistical analysis to determine the associations of variables and the overall quality of life and the four domain scores. A multiple linear regression analysis of the overall quality of life and four domain scores was applied to determine their predictors. RESULTS: The Positive and Negative Syndrome Scale total score, positive symptoms, negative symptoms, disorganized thought, and anxiety/depression showed a significant correlation with the overall quality of life and most of the four domain scores. Depression, SSDs, and adverse drug events showed a significant correlation with a poorer overall quality of life. The multiple linear regression model revealed that negative symptoms, depression, and seeing a relative less often than once per week were predictors for the overall quality of life (adjusted R (2)=0.472). Negative symptoms were also found to be the main factors predicting a decrease in the four domains of quality of life - physical health, psychological, social relationships, and environment. CONCLUSION: Negative symptoms, depression, and poor contact with relatives were the foremost predictors of poor quality of life in individuals with schizophrenia. Positive symptoms, negative symptoms, disorganized thought, anxiety/depression, SSDs, and adverse events were also found to be correlated with quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".