S100. COGNITIVE ENHANCEMENT THERAPY IN SCHIZOPHRENIA: A QUANTITATIVE AND QUALITATIVE ANALYSIS OF PATIENTS’ EXPERIENCES
Bibliographic record
Abstract
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.
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 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".