French Validation of the Screen for Cognitive Impairment in Psychiatry (SCIP-F)
Bibliographic record
Abstract
Background: Measuring cognition in clinical practice is clearly essential to the appropriate characterisation of patients’ clinical status and to the development of a personalised care plan. The Screen for Cognitive Impairment in Psychiatry (SCIP) has been developed in order to provide a brief and accessible tool allowing the evaluation of cognitive function in psychiatric conditions. Objective: We present a validation of a French version of the SCIP. Method: Translation from English into French is carried out using the accepted back-translation method. Seventy-two healthy volunteers are characterised by demographic questionnaires and a neuropsychological battery. The French version of the SCIP is then administered on two separate occasions separated by at least a one-week interval. Results: High internal consistencies as well as strong correlations with comparable neuropsychological tests are obtained. A normalised Cronbach’s α = 0.66 is obtained. Conclusions: The French version of the SCIP (SCIP-F) yields results comparable to the English version. The SCIP represents an essential tool for the preliminary evaluation of cognition. Its characteristics, brevity and the lack of need for a technological platform, allow for its integration into clinical practice. Further testing of SCIP-F in various psychiatric conditions will yield valuable information on its potential in clinical settings.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".