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Record W2600880833 · doi:10.1093/schbul/sbx021.293

215. Action-Based Cognitive Remediation: Pairing Cognitive Training With Skill Development and CBT Principles

2017· article· en· W2600880833 on OpenAlexaff
Christopher R. Bowie, Maya Gupta, Michael Grossman, Michael W. Best, Katherine Holshausen

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsLondon Health Sciences CentreQueen's University
Fundersnot available
KeywordsNeurocognitiveCognitive remediation therapyCognitionPsychologyCognitive skillPsychological interventionCognitive trainingCognitive psychologyCognitive rehabilitation therapyAction (physics)Psychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.155
GPT teacher head0.390
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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