Psychometric properties of the abbreviated version of the Scale to Assess Unawareness in Mental Disorder in schizophrenia
Bibliographic record
Abstract
Background: The Scale to Assess Unawareness in Mental Disorder (SUMD) is widely used in clinical trials and epidemiological studies but more rarely in clinical practice because of its length (74 items). In clinical practice, it is necessary to provide shorter instruments. The aim of this study was to investigate the validity and reliability of the abbreviated version of the SUMD. Methods: Design: We used data from four cross-sectional studies conducted in several psychiatric hospitals in France. Inclusion criteria: a diagnosis of schizophrenia based on DSM-IV criteria. Data collection: socio-demographic and clinical data (including duration of illness, Positive and Negative Syndrome Scale, and the Calgary Depression Scale); quality of life; SUMD. Statistical analysis: confirmatory factor analyses, item-dimension correlations, Cronbach’s alpha coefficients, Rasch statistics, relationships between the SUMD and other parameters. We tested two different scoring models and considered the response ‘not applicable’ as ‘0’ or as missing data. Results: Five hundred and thirty-one patients participated in this study. The 3-factor structure of the SUMD (awareness of the disease, consequences and need for treatment; awareness of positive symptoms; and awareness of negative symptoms) was confirmed using LISREL confirmatory factor analysis for the two models. Internal item consistency and reliability were satisfactory for all dimensions. External validity testing revealed that dimension scores correlated significantly with all PANSS scores, especially with the G12 item (lack of judgement and awareness). Significant associations with age, disease duration, education level, and living arrangements showed good discriminant validity. Conclusion: The abbreviated version of the SUMD appears to be a valid and reliable instrument for measuring insight in patients with schizophrenia and may be used by clinicians to accurately assess insight in clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".