Depression and quality of life in schizophrenia-spectrum
Bibliographic record
Abstract
Introduction The coming out of depressive disorders seems to be associated with severity of schizophrenia’s disease and with poor quality of life (QoL). Objectives The aim of our study was to assess the relationship between depression and QoL in patients with schizophrenia. Methods This is a cross-sectional and analytical study including stabilized patients with schizophrenia or schizoaffective disorder followed up in the outpatient psychiatry department at Hedi Chaker hospital university of Sfax (Tunisia), between August and October 2019. We used the Calgary Depression Scale (CDS) to evaluate depression and the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF) to assess QoL. Results We recruited 37 patients with a mean age of 49.14 years and a sex ratio of 4.66. Seventy-three (73%) of patients were followed for schizophrenia and 27% for schizoaffective disorder. They were married in 43.2% and 35.1% of patients had a regular work. According to CDS, 18.9% of patients had depression with a mean score of 2.27 (SD 2). QLESQSF mean score was 65.51. Depression was negatively correlated with Quality of Life Enjoyment and Satisfaction (r=-0.59, p<0.001). We did not find a significant difference in depression according to the socio-demographic characteristics of the respondents or the clinical features of the disease. Conclusions It is clear that depression in patients with schizophrenia is associated with significant functional disability. Strategies to overcome the burden of depression may instil hope for functional recovery.
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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 | 0.001 |
| 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) | 0.002 | 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".