La SUMD (Scale to Assess Unawareness of Mental Disorder): Validation et adaptation française dans une population de patients souffrant de schizophrénie
Bibliographic record
Abstract
OBJECTIVES: To validate the French version of the Scale to Assess Unawareness of Mental Disorder (SUMD) in patients with schizophrenia. METHOD: One hundred patients with schizophrenic disorders were included. Our statistical analyses evaluated interrater reliability, theoretical validity, and convergent or divergent validity. Finally, an exploratory factor analysis was conducted. RESULTS: The results revealed good psychometric properties for the French version of the SUMD. Both interrater reliability (ICC ranged from 0.68 to 1.00) and internal consistency (Cronbach 0.70) were satisfactory. Criterion validity was confirmed by high correlation values between SUMD scores and scores on the Positive and Negative Syndrome Scale G12 item evaluating insight. Moreover, as hypothesized, there were few associations between SUMD scores and clinical variables. Finally, Principal Component Analyses confirmed the hypothesis of 2 distinct insight dimensions (consciousness and attribution) for both present and past aspects. CONCLUSIONS: This French version of the SUMD is a reliable and valid measure of insight in schizophrenia. The clinical relevance of its measure and the development of psychosocial interventions to improve insight into illness in patients with schizophrenia 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".