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Record W2790873390 · doi:10.1111/jar.12447

Development of a cognitive behavioural therapy‐based guided self‐help intervention for adults with intellectual disability

2018· article· en· W2790873390 on OpenAlexaff
Meg McQueen, Ashleigh Blinkhorn, A. Broad, Jessica Jones, Farooq Naeem, Muhammad Ayub

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2018
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntellectual disabilityThematic analysisCompetence (human resources)Psychological interventionPsychologyCognitionIntervention (counseling)Occupational therapyFocus groupPopulationClinical psychologyQualitative researchMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Despite strong evidence for cognitive behaviour therapy (CBT) in treating mental health, its use, thus far, has been limited for people with intellectual disabilities. This study describes a CBT-based guided self-help (CBT-GSH) manual for individuals with intellectual disability, and focus groups explore the views of clinicians, therapists, support staff and managers. MATERIAL AND METHODS: Using a qualitative methodology, an expert team adapted the manual. Focus groups provided feedback, followed by thematic content analysis for modifications. RESULTS: Participants supported using the manual, with varying views about the delivery. Quality of relationships and competence of the administrator determined the best person to deliver the treatment. Heterogeneity in the intellectual disability population was a challenge to delivering manual-based interventions. Participants made suggestions about language and organization. CONCLUSIONS: Amendments were made to the manual in line with expert feedback. An evaluation is warranted to test for feasibility, delivery, acceptability and efficacy.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.191
GPT teacher head0.422
Teacher spread0.231 · 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 designNon-randomized trial
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

Citations9
Published2018
Admission routes1
Has abstractyes

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