A structural equation modelling approach to explore the determinants of ă quality of life in schizophrenia
Bibliographic record
Abstract
Objective: This study aimed to analyse the relationships among psychotic a symptoms, depression, neurocognition and functioning as determinants of a quality of life (QoL) in patients with schizophrenia. a Methods: In this cross-sectional study, we evaluated QoL with the a Schizophrenia Quality of Life 18-item scale (S-QoL 18), neurocognition a with multiple tests exploring memory, attention and executive functions, a the severity of psychotic symptoms with the Positive and Negative a Syndrome Scale (PANSS), depression with the Calgary Depression Scale for a Schizophrenia (CDSS) and functioning using the Functional Remission Of a General Schizophrenia (FROGS) scale. We used Structural Equation a Modelling (SEM) to describe the relationships among the severity of a psychotic symptoms, depression, neurocognition, functioning and QoL. a Results: Two hundred and seventy-one outpatients with schizophrenia a participated in our study. SEM showed good fit with chi(2)/df = 1.97, a root mean square error of approximation = 0.06, comparative fit index = a 0.93 and standardized root mean square residuals = 0.05. This model a revealed that depression was the most important feature associated with a QoL, mainly for the self-esteem, autonomy and resilience dimensions a (direct path coefficient = -0.46). The direct path between functioning a and QoL was also significant (path coefficient = 0.26). The severity of a psychotic symptoms and neurocognitive impairment were weakly and a indirectly associated with QoL via functioning (path coefficients = a -0.18 and 0.04, respectively). a Conclusions: This study contributes to a better understanding of the a determinants of QoL in schizophrenia. Our findings should be considered a in developing effective strategies for improving QoL among this a population. (C) 2016 Elsevier B.V. All rights reserved.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".