Insight as a mediator between stigma and depression in schizophrenia
Bibliographic record
Abstract
Introduction The “Paradox of insight” in schizophrenia is a fact of its controversial impact with both positive and negative sides. It is known that insight and depression are positively correlated: the more awareness of schizophrenic illness, the more likelihood of depression. The mechanisms of this correlation have been clarified insufficiently at the moment. Objective We hypothesized that the correlation between level of depression in patients and stigmatizing views of their close relatives depends on patient's illness awareness. Materials and methods 120 patients (response rate - 80%) with a diagnosis of “paranoid schizophrenia” were included in the cross-sectional, observational study. Following questionnaires were used: “The Scale to Assess Unawareness of Mental Disorder” (SUMD), “Calgary Depression Scale for Schizophrenia” (CDSS). The stigmatizing views were assessed in patient's closest relatives with questioner “Mental health in public conscience” developed by Iastrebov et all. Results We have found statistically significant differences of correlations between patients’ groups with different level of insight (full, partial and absence of awareness of mental illness). Moreover, only in group of patients with full awareness of mental disorder the statistically significant correlation between level of depression in patients and intensity of stigmatizing beliefs in their close relative was found. Conclusions Received data support hypothesis that the correlation between the level of depression in patients and the intensity of stigmatizing views of their close relatives depends on the patient's illness awareness.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".