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Record W4308308729 · doi:10.1016/j.scog.2022.100272

Development and validation of a fidelity instrument for Cognitive Adaptation Training

2022· article· en· W4308308729 on OpenAlexaff
Michelle van Dam, Jaap van Weeghel, Stynke Castelein, Annemarie Stiekema, Piotr J. Quee, Sean A. Kidd, Kelly Allott, Natalie Maples, Dawn I. Velligan, Marieke Pijnenborg, Lisette van der Meer

Bibliographic record

VenueSchizophrenia Research Cognition · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersStichting tot Steun Vereniging tot Christelijke Verzorging van Geestes- en Zenuwzieken
KeywordsFidelityAdaptation (eye)PsychologyCognitionComputer scienceCognitive psychologyApplied psychologyNeuroscience

Abstract

fetched live from OpenAlex

Purpose: Cognitive Adaptation Training (CAT) is a psychosocial intervention with demonstrated effectiveness. However, no validated fidelity instrument is available. In this study, a CAT Fidelity Scale was developed and its psychometric properties, including interrater reliability and internal consistency, were evaluated. Methods: The fidelity scale was developed in a multidisciplinary collaboration between international research groups using the Delphi method. Four Delphi rounds were organized to reach consensus for the items included in the scale. To examine the psychometric properties of the scale, data from a large cluster randomized controlled trial evaluating the implementation of CAT in clinical practice was used. Fidelity assessors conducted 73 fidelity reviews at four mental health institutions in the Netherlands. Results: After three Delphi rounds, consensus was reached on a 44-item CAT Fidelity Scale. After administration of the scale, 24 items were removed in round four resulting in a 20-item fidelity scale. Psychometric properties of the 20-item CAT Fidelity Scale shows a fair interrater reliability and an excellent internal consistency. Conclusions: The CAT fidelity scale in its current form is useful for both research purposes as well as for individual health professionals to monitor their own adherence to the protocol. Future research needs to focus on improvement of items and formulating qualitative anchor point to the items to increase generalizability and psychometric properties of the scale. The described suggestions for improvement provide a good starting point for further development.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.312
GPT teacher head0.414
Teacher spread0.102 · 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 designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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