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Record W2794678035 · doi:10.1093/schbul/sby014.075

19.1 FROM CLINICAL TRIAL TO THE CLINIC: OPTIMIZING COGNITIVE ADAPTATION TRAINING FOR CASE MANAGEMENT TEAMS

2018· article· en· W2794678035 on OpenAlexaff
Sean A. Kidd, Yarissa Herman, Gursharan Virdee, Chris Bowie, Dawn I. Velligan, Natalie Maples

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's UniversityCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsPopulationPsychological interventionIntervention (counseling)PsychologyCognitive remediation therapyRandomized controlled trialOutreachSchizophrenia (object-oriented programming)CognitionMedicineClinical psychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Cognitive Adaptation Training (CAT) has consistently demonstrated effectiveness in enhancing community functioning in clinical trials of its 9-month application by a specialist. This is a compelling development in the field as clinicians struggle to support gains in independent functioning among patients with schizophrenia. However, outreach interventions delivered by specialists are difficult to support in many contexts where investment in mental health care is insufficient for population needs. This presentation will describe research and implementation efforts that support the delivery of CAT in routine clinical practice. This program of work began with a feasibility study of a modified version of CAT. CAT was modified to decrease the duration of specialist-delivered CAT to 4 months, with the intervention subsequently supported by the individual’s case manager who received rudimentary training and could consult specialists. Twenty-three people with schizophrenia participated in this study of symptom and functional outcomes, evaluating improvements after 4 months of CAT specialist intervention and after an additional 5 months of case manager support. Also described briefly will be (i) preliminary findings from a superiority randomized controlled trial of modified CAT in an early intervention population comparing CAT (n=25) with Action Based Cognitive Remediation (n=23) and (ii) efforts to build out CAT implementation in a tertiary facility enabled through the above clinical trial resources. Analysis of feasibility study findings revealed significant improvements in adaptive functioning, psychiatric symptomatology, and goal attainment that were maintained throughout case management follow-up. Effect sizes for the specialist delivered period ranged from .33 (negative symptoms) to 2.01 (goal attainment scaling) with a modest decline in the follow-up period with community functioning remaining at ES=.66. Improvement in the large effect size range was also observed in community functioning in the trial of modified CAT in early intervention. In this period over 70 allied health clinicians were intensively training in CAT locally and regionally and a community of practice was established. These impacts were further extended through the development of an open-access CAT guide for families that can be used independently or with clinician support. This study supports a model for extending the accessibility of CAT in settings that might not otherwise sustain the intervention as it was originally designed. Functional impacts similar to the original clinical trials were observed in a briefer period of specialist delivered CAT and show the promise of being largely sustained over an indefinite period by rudimentary-trained case managers in a consultation model. This observation would appear to apply to both early intervention and general schizophrenia populations. Additionally, this program of work has demonstrated how research-practice synergies can foster implementation that can be sustained after initial research investments.

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)0.015
metaresearch head score (Gemma)0.033
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0700.009

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 head0.136
GPT teacher head0.407
Teacher spread0.271 · 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
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
Published2018
Admission routes1
Has abstractyes

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