F82. COGNITIVE BIASES IN PATIENTS WITH SCHIZOPHRENIA AND HIGH SCHOOL STUDENTS: ASSOCIATION WITH PSYCHOPATHOLOGICAL SYMPTOMS
Notice bibliographique
Résumé
Some cognitive biases, mainly the “jumping to conclusions” and attributional styles, play a key role in the formation and maintenance of delusions. Other thinking errors include dichotomous thinking, emotionally based reasoning, and catastrophising. The aim of our study was to assess the relationship between cognitive biases and psychopathological symptoms (positive, negative, depressive) in a clinical sample of patients with schizophrenia and a population sample of high school students. The clinical sample included 35 patients with schizophrenia (35.6 ± 10.8 years, 40% women) attending to the Department of Mental Health from Parc Taulí Hospital Universitari (Sabadell, Spain) and 45 high school students (16.6 ± 0.9 years) located in the same province. Cognitive biases were assessed with the Cognitive Biases Questionnaire, that covers 5 types of biases (intentionalising [I]; catastrophizing [C]; dichotomous thinking [DT]; jumping to conclusions [JTC]; and emotional reasoning [ER]) and also gives a total score. Psychopathological symptoms in patients were assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). Psychopathological symptoms in high school students were assessed with the Community Assessment of Psychic Experiences (CAPE). Statistical analyses were performed with SPSS version 21.0. CBQ scores between groups were compared with Student’s T-test. The association between dimensions of the CBQ and scores of psychopathological scales was tested with Spearman’s correlations. Significance level was set at p<0.05 (two-tailed). CBQ total scores did not differed between patients with schizophrenia (45.3 ± 8.2) and high school students (44.2 ± 6.7). No significant differences between groups were found in any of the five cognitive biases. When exploring the relationship between cognitive biases and psychopathological symptoms in patients with schizophrenia, total CBQ scores were associated with CDSS scores (r= 0.65, p<0.001). In relation to particular cognitive biases, depressive symptoms were associated with all cognitive biases (I: r= 0.43, p= 0.017; C: r= 0.62, p<0.001; DT: r= 0.42, p= 0.020; JTC: r= 0.46, p= 0.012; ER: r= 0.57, p= 0.001), positive symptoms with ER (r= 0.43, p= 0.009) and general psychopathology symptoms of the PANSS with C (r= 0.34, p= 0.044), DT (r= 0.35, p= 0.041) and ER (r= 0.45, p= 0.007). In high school students, CBQ total scores were associated with positive (r= 0.43, p= 0.003) and depressive (r= 0.35, p= 0.020) symptoms. In relation to particular cognitive biases, depressive symptoms were associated with DT (r= 0.47, p= 0.001) whereas positive symptoms were associated with DT (r= 0.31, p= 0.036) and ER (r= 0.30, p= 0.047). Although we did not find significant differences in the presence of cognitive biases when comparing two different samples, similar associations were found when exploring the relationship between cognitive biases and psychopathology symptoms. Our results are in accordance previous studies reporting the role of some cognitive biases on the risk of developing psychotic symptoms. On the other hand, a clear association between cognitive biases was found for depressive symptoms in both patients with schizophrenia and high school students. Our study highlights the importance of identifying and treating cognitive biases with appropriate therapies (e.g. metacognitive training) for improving the outcome of psychoses in both patients and people at risk for developing a psychotic disorder in the future.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».