The Insight Paradox: is Better Insight Associated with Depression Among Patients with Schizophrenia?
Bibliographic record
Abstract
The insight paradox posits that among patients with schizophrenia, better insight is associated with depressive symptoms. However, available studies are characterized by conflicting results. First, we conducted a systematic review, a meta-analysis and a meta-regression based on 59 available correlational studies. Second, we examined a cross-sectional examination on 80 patients diagnosed with schizophrenia in stable phase of the illness. Measures of depressive dimension were based on the Calgary Depression Scale for Schizophrenia (CDSS) and Beck Depression Inventory (BDI), for insight the Scale to assess Unawareness of Mental Disorder (SUMD). Furthermore, we assessed self-stigma, self-esteem and psychotic symptoms to test mediating and moderating models (Preacher and Hayes models). In the meta-analysis, global insight was associated weakly, but significantly with depression (effect size r=0.14), as were the insight into the mental disorder (r=0.14), insight into symptoms (r=0.14) and symptoms’ attributions (r=0.17). Whereas, insight into the social consequences of the disorder or into the need for treatment were not associated with symptoms of depression. Better cognitive insight was associated with higher levels of depression. Methodological and clinical factors moderated the magnitude of the association between insight and depression. Similar results were observed in the clinical sample, where self-stigma significantly mediated the association between insight and depression. In conclusion, both literature and clinical findings indicate that better insight is associated with higher levels of depressive symptoms among patients with schizophrenia: interventions that are aimed at improving insight need to take into account the implications of these findings
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".