Current Trends in the Empirical Study of Cognitive Remediation for Schizophrenia
Bibliographic record
Abstract
Cognitive remediation (CR) for schizophrenia is a learning-based behavioural skills training intervention designed to enhance neuro and (or) social cognitive skills, with the ultimate goal of generalization to improve psychosocial outcomes. This review summarizes conceptual approaches to CR for schizophrenia and the evidence for efficacy in clinical research settings. Four issues are at the forefront of ongoing research: the identification of techniques that produce the largest cognitive change, delineation of techniques that enhance transfer of cognitive skills to functional skills, the identification of CR methods that can be personalized to meet the specific cognitive and functional needs of each individual, and, all the while, ensuring that when CR methods are developed in a research setting, they remain scalable for delivery in the larger clinical community. In response to these issues, 3 prominent research trends have emerged: the rise of a new generation of computerized restorative cognitive training, the integration of CR with skills training to promote generalization, and the application of techniques to enhance motivation and learning during CR. As data on the neural basis of learning in people with schizophrenia become available, new technologies that harness the ability of the brain to make sustainable, functional changes may be integrated within a therapeutic context that promotes a personalized approach to learning. The development of transportable and scalable methods of CR that maximize the ability of people with schizophrenia to improve cognition will help them achieve personal goals for recovery.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".