The Role of Insight in Moderating the Association Between Depressive Symptoms in People With Schizophrenia and Stigma Among Their Nearest Relatives: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: There is evidence of a positive association between insight and depression among patients with schizophrenia. Self-stigma was shown to play a mediating role in this association. We attempted to broaden this concept by investigating insight as a potential moderator of the association between depressive symptoms amongst people with schizophrenia and stigmatizing views towards people with mental disorders in their close social environment. METHOD: In the initial sample of 120 pairs, data were gathered from 96 patients with a diagnosis of "paranoid schizophrenia" and 96 of their nearest relatives (80% response rate). In this cross-sectional study data were collected by clinical interview using the following questionnaires: "The Scale to Assess Unawareness of Mental Disorder," "Calgary Depression Scale for Schizophrenia," and "Brief Psychiatric Rating Scale." The stigmatizing views of patients' nearest relatives towards people with mental disorders were assessed with the "Mental Health in Public Conscience" scale. RESULTS: Among patients with schizophrenia depressive symptom severity was positively associated with the intensity of nearest relatives' stigmatizing beliefs ("Nonbiological vision of mental illness," τ = 0.24; P < .001). The association was moderated by the level of patients' awareness of presence of mental disorder while controlling for age, sex, duration of illness and psychopathological symptoms. CONCLUSIONS: The results support the hypothesis that the positive association between patients' depression and their nearest relatives' stigmatizing views is moderated by patients' insight. Directions for further research and practical implications are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".