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Record W1993629603 · doi:10.3390/bs4030301

Working Memory Training in Schizophrenia and Healthy Populations

2014· review· en· W1993629603 on OpenAlexafffund
Linette Lawlor‐Savage, Vina M. Goghari

Bibliographic record

VenueBehavioral Sciences · 2014
Typereview
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsSchizophrenia (object-oriented programming)Generalizability theoryCognitive trainingCognitionCognitive remediation therapyPsychologyWorking memoryWorking memory trainingIntervention (counseling)Cognitive psychologyCognitive skillClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Cognitive deficits are consistently demonstrated in individuals with schizophrenia. Cognitive training involves structured exercises prescribed and undertaken with the intention of enhancing cognitive abilities such as attention, memory, and problem solving. Thus, cognitive training represents a potentially promising intervention for enhancing cognitive abilities in schizophrenia. However, cognitive training programs are numerous and heterogeneous, hence, the generalizability of training related outcomes can be challenging to assess. This article will provide a brief overview of current literature on cognitive training and explore how knowledge of working memory training in healthy populations can potentially be applied to enhance cognitive functioning of individuals with schizophrenia.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.495
GPT teacher head0.507
Teacher spread0.012 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2014
Admission routes2
Has abstractyes

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