Sélection du Matériel Pour L'élaboration D'un Test de Stroop Émotionnel Adapté Aux Troubles Schizophréniques et Bipolaires
Bibliographic record
Abstract
OBJECTIVE: The emotional Stroop test evaluates the influence of the emotional valence of stimuli on cognitive inhibition processes. In subjects with psychiatric disorders, interference increases in this test when valence refers to their specific psychopathology. This study aims to develop a version of the emotional Stroop test adapted to paranoid schizophrenia and bipolar disorder. METHOD: The emotional valence and the number of times patients used 200 words related to schizophrenia and bipolar disorder psychopathology were assessed by 25 clinicians; then a principal component analysis was performed with an ascending hierarchical classification. RESULTS: Words are distributed according to 2 factorial dimensions, emotionality and tonality, into 4 valence classifications: depressive, paranoid, manic, and neutral words. There were no differences in the lexical frequency of the words chosen to develop the test. CONCLUSIONS: The statistical validation of the emotional valence of words allows for the development of an emotional Stroop test adapted to exploring emotional bias in paranoid schizophrenia and bipolar disorder.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".