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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".