215. Action-Based Cognitive Remediation: Pairing Cognitive Training With Skill Development and CBT Principles
Bibliographic record
Abstract
Background: Most people with mental disorders experience a reduction of symptoms with treatment, but recovery of everyday functions is often delayed and incomplete. Cognitive Remediation is widely recognized as an efficacious treatment that improves attention, memory, and executive functions, but its ability to effectively promote behavior change is more limited and retention in treatment is often low. In response, we developed Action-Based Cognitive Remediation (ABCR) to build on intact procedural learning skills, pair neurocognitive training with skill-based training, and promote engagement in everyday behaviors considered cognitively challenging. We sought to determine if ABCR was more efficacious (improved neurocognition) and more effective (improved functional skills and vocational outcomes) than traditional cognitive remediation. Methods: In this study, we compared ABCR to a traditional form of cognitive remediation in 50 participants with severe mental disorders. Treatment was 10 weeks, twice per week, in a group format. Both treatments provided computerized cognitive training, discussions of how to monitor and flexibly adapt strategies when solving problems, and discussions of how cognitive skills and strategies can be used in everyday life. Compared to traditional cognitive remediation, ABCR also included role-plays in simulated work tasks and goal setting with an emphasis on seeking cognitive challenge in everyday life. Results: Cognitive response was moderate to large and statistically significant for both groups. and the ABCR group demonstrated larger improvements in a role-play measure of functional skills (P < .001). A statistical trend was observed for more participants in the ABCR group working at 6 months postintervention (P = .09) and, among those working, ABCR participants reported less job stress (P = .03). ABCR was more tolerable, with 83% retention rates compared to 57% for traditional cognitive remediation (P = .03). Conclusion: These results support the placing of cognitive training within a broader skill training and psychotherapeutic milieu that encourages approaching cognitively challenging activities and reducing withdrawal from social and instrumental tasks. Compared to the more passive experience of traditional cognitive remediation, ABCR challenges participants to engage with their environment and produces larger and more lasting changes in behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".