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Record W7116302558 · doi:10.7870/cjcmh-2025-015

Remote Cognitive Remediation Combined With Supported Education in First-Episode Psychosis: Challenging, Yet Promising

2025· article· en· W7116302558 on OpenAlexafffundvenue
Élisabeth Thibaudeau, Ariane Giguère-Rancourt, Tania Lecomte, Marc Corbière, Amal Abdel‐Baki, Audrey Cayouette, Roy Marc-André, Amélie Achim, Martin Lepage, Til Wykes, Matteo Cella, Clare Reeder, Caroline Cellard

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2025
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre Jeunesse de QuebecMcGill UniversityDouglas CollegeCentre Hospitalier de l’Université de MontréalUniversité du Québec à MontréalUniversité LavalInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalInstitut universitaire en santé mentale de Montréal
FundersCanadian Institutes of Health Research
KeywordsCognitive remediation therapyPsychosocialCognitionPsychosisCognitive skill

Abstract

fetched live from OpenAlex

Education is an important goal in first-episode psychosis (FEP), which can be complicated by cognitive impairments. Combining cognitive remediation (CR) and supported education (SE) could improve education prospects. This study aimed to assess the feasibility and acceptability of remote CR+SE in FEP, and to explore its effect on cognitive and psychosocial outcomes. Nine participants were recruited, of which four completed the intervention. Overall, they found the CR program acceptable. Reliable Change Indices revealed significant improvements for cognitive functioning and self-esteem, and participants partially or completely reached their goals. Remote CR+SE in FEP appears promising, though engagement remains a challenge.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.355
Teacher spread0.326 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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