S100. COGNITIVE ENHANCEMENT THERAPY IN SCHIZOPHRENIA: A QUANTITATIVE AND QUALITATIVE ANALYSIS OF PATIENTS’ EXPERIENCES
Notice bibliographique
Résumé
Evidence shows that cognitive remediation therapy helps improve cognition in people with schizophrenia. Previous studies have demonstrated mild to moderate positive effects of cognitive remediation therapy, but often with a high attrition rate. Furthermore, the impact of cognitive remediation therapy on patients’ lives following treatment completion remains unclear. Systematic exploration of patients’ perspectives on cognitive remediation therapy will allow for better understanding of its impact and the factors influencing adherence to treatment. This quantitative and qualitative descriptive study aimed to identify factors that influence patients’ experiences going through a comprehensive cognitive remediation treatment called Cognitive Enhancement Therapy (CET). CET is designed to provide enriched cognitive experiences by combining individual therapy with neurocognitive training and skills group therapy. We recruited 9 patients who have completed CET (mean age: 26.8, 8 men) and we performed two semi-structured focus groups, as well as one individual interview. We assessed three inductive themes when analyzing responses: 1) Motivational factors, 2) Experiences with the CET program, and 3) Impacts of the treatment. All participants also answered questionnaires on their current life satisfaction and subjective impression on CET. Patients reported that family support and subjective feeling of improvement during CET were two motivational factors in choosing to attend and complete the treatment. The size and lighting of the room where treatment took place were also reported to influence motivation. When asked about their experiences with the CET program, participants mentioned that they liked learning and being challenged. Participants specifically raised the importance of learning strategies during the computerized exercises portion of the treatment, as well as the importance of receiving feedback overall. Every participant mentioned that carrying the CET educational binder reduced motivation and that it could be improved in the future. When asked about long-term impacts of the treatment, patients reported that CET improved focus and confidence, and helped facilitate successful peer interactions. Patients also mentioned that CET made them more confident to go back to school or apply for a job. All participants reported that CET helped them (a lot: 55.6%, a good amount: 44.4%), and the majority reported enjoying their participation in CET (a good amount: 55.6%, a lot: 33.3%). Most participants who completed CET reported high satisfaction with their current work/school situation (55.6%) but reported less satisfaction with their social life (66.7%). Patients’ perspectives on CET can guide future cognitive remediation trials. Simple aspects such as the treatment setting or the quality of the educational materials can make a difference in patients’ motivation and satisfaction. Our results highlight the importance of providing learning strategies and constant feedback to the patients during the course of the treatment. CET seems to improve patients’ self-reported focus and helps them to achieve their professional and academic goals. Our findings also suggest that promoting family support could increase motivation to pursue and complete CET. Further work is needed to help improve patients’ social lives after treatment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».