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Record W2963326094 · doi:10.1016/j.scog.2019.100157

A comparison of compensatory and restorative cognitive interventions in early psychosis

2019· article· en· W2963326094 on OpenAlexafffund
Sean A. Kidd, Yarissa Herman, Gursharan Virdee, Christopher R. Bowie, Dawn I. Velligan, Christina Plagiannakos, Aristotle N. Voineskos

Bibliographic record

VenueSchizophrenia Research Cognition · 2019
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's UniversityCentre for Addiction and Mental HealthUniversity of Toronto
FundersCentre for Addiction and Mental Health FoundationCentre for Addiction and Mental Health
KeywordsNeurocognitivePsychological interventionPsychologyCognitionClinical psychologyPsychosisRandomized controlled trialPopulationCognitive remediation therapyPsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This randomized trial examined the relative effectiveness of primarily compensatory and primarily restorative cognitive interventions in an early psychosis population. A total of 56 patients were randomized to one of two treatments which were applied for four months with a five month follow up assessment. Comparisons were between (1) Cognitive Adaptation Training (CAT) - a treatment that uses environmental supports and weekly home visits to compensate for cognitive challenges and improve community functioning and (2) Action Based Cognitive Remediation (ABCR) - a treatment involving computerized cognitive drill and practice exercises, simulations, goal setting, and behavioral activation. Linear mixed effects models demonstrated significant effects on community functioning for both CAT and ABCR without a difference between conditions (n = 39), with an indication of greater gains at follow up in the ABCR group (n = 31). Improvements in symptomatology were less robust with mixed findings across neurocognition metrics. This study concluded that both CAT and ABCR hold promise as interventions for early intervention psychosis populations but more work is needed to identify illness severity, subtype and contextual considerations that might indicate an emphasis on more compensatory versus more restorative cognitive interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.453
Teacher spread0.324 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2019
Admission routes2
Has abstractyes

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