Variables Influencing Subjective Well-Being in Patients with Schizophrenia
Bibliographic record
Abstract
Objectives : The purpose of this study was to analyze the relationship between subjective well-being and other clinical parameters such as sociodemographic and clinical variables, which include positive and negative symptoms, depressive symptoms, insight, and side effects.Methods : Fifty-one outpatients diagnosed with schizophrenia were recruited in this study.Subjective well-being was assessed using a self-rating scale, the Subjective Well-being under Neuroleptics-Short form (SWN-K). Sociodemographic variables were also evaluated and other evaluations were conducted using the Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), Liverpool University Neuroleptic Side Effect Rating Scale (LUNSERS), Korean Version of the Revised Insight Scale for Psychosis (KISP), and Multidimensional Scale of Perceived Social Support (MSPSS).The relationship between subjective well-being and these clinical variables was assessed.Results : Education years and social support scores were positively correlated with the total SWN-K scores, but severity of illness, severity of depression, severity of side effect, and the scores on insight were negatively correlated.The stepwise multiple regression analyses indicated that the total SWN-K score of the patients with schizophrenia was associated with negative symptoms and insight.Conclusion : Better insight and more severe negative symptoms in patients with schizophrenia may be associated with worse subjective well-being.Results indicate that careful evaluation of subjective well-being is essential for proper management of patients 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".