Mapping Insight Dimensions and Symptom Dynamics in Schizophrenia: A Data-Driven Network Approach: Cartographie des dimensions d’insight et de la dynamique symptomatique dans la schizophrénie: une approche par réseau fondée sur les données
Bibliographic record
Abstract
(SSD) present with cognitive, behavioral, and emotional difficulties. Affected individuals often exhibit poor insight into aspects of their illness, such as awareness of the illness itself or the need for treatment, which can hinder treatment adherence and complicate clinical outcomes. This study aimed to investigate the relationships between clinical symptoms and dimensions of insight in SSD using a network approach, which captures direct and indirect relationships among variables. We hypothesized that illness awareness would correlate negatively with positive symptoms and positively with depressive symptoms, and that positive symptoms would have the strongest influence on the network.MethodsData were collected from 142 individuals diagnosed with SSD. Insight was measured using the Birchwood Insight Scale (IS) across three dimensions: illness awareness, symptom re-labelling, and awareness of the need for treatment. Symptoms were evaluated using the Scale for the Assessment of Positive Symptoms, the Scale for the Assessment of Negative Symptoms, the Calgary Depression Scale and the Hamilton Anxiety Scale. Network analysis was employed to explore interconnections (edges) between variables (nodes) and identify influential variables through centrality measures (strength, betweenness, closeness).ResultsA significant positive connection was found between illness awareness and depressive symptoms. Anxiety and depressive symptoms were identified as the most central and influential variables within the network. Treatment awareness showed greater centrality than illness awareness, indicating this dimension's potential importance in influencing symptom dynamics in a clinical profile.ConclusionsAnalyzing a more extensive network that includes treatment adherence and cognitive domains affected in SSD could enhance and validate the understanding of the cascading effects of symptoms and insight dimensions, allowing for more tailored treatments.Plain Language Summary TitleInterconnections between levels of awareness and clinical symptoms in schizophrenia and related disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".