Insight, self-stigma and depressive symptoms among patients with schizophrenia
Bibliographic record
Abstract
Depressive symptoms are rather prevalent among patients with schizophrenia and various factors can influence them. Insight and self-stigma shows complex and contradictory relationship, both are among most important features of schizophrenia with strong impact on depressive symptoms. We wanted to test hypothesis that preserved insight is related to depressive symptoms only when self-stigma is also high. Our cross-sectional research comprised 149 patients with diagnosis of schizophrenia, both gender, age span 25-45 years. Rating scales used were Calgary Depression Scale (CDS), Scale to assess Unawareness of Mental Disorder (SUMD) and Internalized Stigma in Mental Illness (ISMI) which are specifically designed for patients with schizophrenia. Majority of patients were male (72%), single or separated (85%). Relation between selfstigma and depressive symptoms was statistically significant ( b =0.12, 95% CI=[0.06, 0.19], β=0.32, t (135)=3.89, P b =–0.01, 95% CI=[–0.02,–0.001], β=–0.17, t (135)=–2.20, P =0.029). Post-hoc analysis showed that among patients with extremely high selfstigma (more than 90 centile), higher insight was related to more depressive symptoms (b=–0.22, 95% CI=[–0.42,–0.02], β=–0.34, t =–2.23, P =0.028). These results are important for tailoring specific antistigma programs for patients with high level of insight in order to prevent deleterious impact of depressive symptoms on course of 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 | 0.001 | 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".