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Record W3081000143 · doi:10.2196/17412

Evaluation of an Adaptive Implementation Program for Cognitive Adaptation Training for People With Severe Mental Illness: Protocol for a Randomized Controlled Trial

2020· article· en· W3081000143 on OpenAlexvenueno aff
Michelle van Dam, Jaap van Weeghel, Stynke Castelein, Gerdina Hendrika Maria Pijnenborg, Lisette van der Meer

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersStichting tot Steun Vereniging tot Christelijke Verzorging van Geestes- en Zenuwzieken
KeywordsMental healthPsychosocialRandomized controlled trialAdaptation (eye)Implementation researchService delivery frameworkCognitionIntervention (counseling)PsychologyMedicineService (business)NursingPsychological interventionPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive Adaptation Training is a psychosocial intervention that focuses on reducing the negative effects of cognitive disorders, especially executive functions such as planning and targeted action. International research has shown that Cognitive Adaptation Training enhances multiple aspects of daily functioning in people with severe mental illnesses. Despite this evidence, implementation of the intervention into routine care remains a challenge. OBJECTIVE: In this implementation research, a newly developed implementation program based on previous experience and scientific literature, is tested. The primary aim of this research is to assess the effectiveness of the implementation program. The secondary aim of this study is to evaluate the factors that impede or facilitate the implementation of Cognitive Adaptation Training. METHODS: To test the effectiveness of the implementation program, a multicenter cluster randomized controlled trial was conducted comparing the implementation program to a single training program in four mental health institutions in The Netherlands. Focus groups, semistructured interviews, and questionnaires were used at multiple levels of service delivery (service user, professional, team, organization) to identify factors that may hamper or facilitate implementation. The RE-AIM framework was applied to measure the implementation effectiveness. Following this framework, the primary outcomes were Reach, Intervention Effectiveness, Adoption, Implementation, and Maintenance. These are assessed before, during, and after implementation. The research had a total duration of 14 months, with a follow-up measurement at 14 months. Data will be analyzed using multilevel modeling. RESULTS: The study was funded in April 2018. Data collection occurred between November 2018 and January 2020. In total, 21 teams of 4 mental health institutions agreed to participate. Data analysis is ongoing and results are expected to be published in December 2020. CONCLUSIONS: This implementation research may provide important information about the implementation of psychosocial interventions in practice and may result in a program that is useful for Cognitive Adaptation Training, and possibly for psychosocial interventions in general. TRIAL REGISTRATION: The Netherlands Trial Register (NL7989); https://www.trialregister.nl/trial/7989. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17412.

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)0.046
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.059
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.044
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0140.007
Bibliometrics0.0040.004
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0590.010

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 head0.366
GPT teacher head0.593
Teacher spread0.227 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations3
Published2020
Admission routes1
Has abstractyes

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