The Effectiveness of Cognitive Rehabilitation on Improving Cognitive Deficits in Patients With Chronic Schizophrenia Based on Cognitive Levels
Bibliographic record
Abstract
Objectives Cognitive deficits are a core feature of schizophrenia, which are directly associated with the functional and social outcomes of this disorder. Cognitive remediation therapy aims to improve the deficits and the following outcomes. This study aimed to investigate the effectiveness of cognitive remediation therapy on attention and working memory of two groups of patients with schizophrenia (low and moderate cognitive deficit). Methods A total of 30 hospitalized patients with schizophrenia were selected and divided into two different groups by clinical interviews and the scores obtained in the montreal cognitive assessment (MoCA). The patients with low and moderate cognitive deficits were evaluated by the classic Stroop test, continuous performance test (CPT), and n-back test before and after the treatment. Both groups received cognitive remediation therapy prepared by Sholberg and Mateer (2001). This rehabilitation program in treating patients with schizophrenia focuses on the cognitive abilities of memory and its elements, attention, and dimensions of attention and executive function. The above rehabilitation program has been prepared for individual or group implementation, and its purpose is to repair cognitive deficits and skills through practice and training. The number of sessions of this program includes 16, and the instructions for each session are very specific. Each session takes an average of 30 to 45 minutes. Results The results showed that cognitive rehabilitation in both groups at the post-test level (P≤0.05) improved cognitive performance significantly in the areas of sustained attention, selective attention, and working memory. Comparing the performance between the two groups, only a significant difference (P≤0.05) was observed between the two groups in the field of sustained attention. Conclusion Based on the findings, cognitive rehabilitation treatment improves patients’ performance in selective attention, sustained attention, and working memory. In addition, in terms of the effectiveness of cognitive rehabilitation between the two groups with low and moderate cognitive deficits, the findings indicate more progress in the group with more severe cognitive deficits in the field of sustained attention and no difference in selective attention performance and working memory.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".