Screening for cognitive impairment in schizophrenia: Psychometric properties of the German version of the Screen for Cognitive Impairment in Psychiatry (SCIP-G)
Bibliographic record
Abstract
BACKGROUND: The Screen for Cognitive Impairment in Psychiatry (SCIP) is a brief scale designed for detecting cognitive deficits in patients with psychiatric disorders including schizophrenia. In this preliminary study the psychometric properties of the German version of the SCIP are examined in a sample of patients with schizophrenia and schizoaffective psychosis (DSM-IV) as well as in healthy controls. METHODS: Thirty patients and thirty matched controls were asked to complete two versions of the SCIP separated by two-week intervals in addition to psychiatric and neurocognitive instruments including assessments to measure psychosocial functioning. Feasibility, reliability and validity of the SCIP were examined in order to determine parallel reliability. The convergent validity was assessed by the BACS (Brief Assessment of Cognition in Schizophrenia) and the MMSE (Mini-Mental-State-Examination). RESULTS: Significant differences in cognitive performance between patients and healthy controls were detected in both versions of the SCIP. The SCIP effectively discriminated between patients and the control sample. The reliability of the parallel versions of the SCIP was supported by high correlations between the alternate forms, and by the high internal consistency of SCIP subtests within the patient sample. Construct validity of the SCIP was supported by high correlations between the SCIP and the BACS total scores, and by high correlations with common cognitive domain scores from the two tests. CONCLUSIONS: Our data show that the German version of the SCIP (SCIP-G) is a brief, valid and reliable assessment tool for the detection of cognitive impairment in patients with schizophrenia or schizoaffective psychosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".