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Record W2097555479 · doi:10.1002/cpp.621

DIRT—danger ideation reduction therapy for obsessive–compulsive washers: a comprehensive guide to treatment

2009· article· en· W2097555479 on OpenAlexaff
Kieron O’Connor

Bibliographic record

VenueClinical Psychology & Psychotherapy · 2009
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsDirtPsychologyIntervention (counseling)PsychotherapistObsessive compulsiveIdeationPsychiatryCognitive science

Abstract

fetched live from OpenAlex

Abstract Danger ideation reduction therapy (DIRT) for obsessive compulsive disorder (OCD) is a new intervention focusing on providing corrective information, and is the subject of a new comprehensive guide to treatment for compulsive washing. The components of DIRT are well presented in this manual‐based treatment and the documentation includes dialogues, filmed interviews with workers in dangerous occupations, and fact sheets to persuade the client to exchange beliefs about danger for beliefs about safety. The book is well organized and user friendly. Clinical trials have shown DIRT to be an effective treatment. Although DIRT as a stand alone therapy seems to offer some advantages over conventional CBT, it may function currently more as an adjunct to help cognitive restructuring. DIRT certainly encourages us to rethink some assumptions about the use of corrective information in treating OCD. Copyright © 2009 John Wiley & Sons, Ltd. Key Practitioner Message: • Danger ideation therapy is principally a cognitive approach. • Corrective information may help in the treatment of OCD for washing. • The application of DIRT in other types of OCD and symbolic contamination remains uncertain.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.094
GPT teacher head0.487
Teacher spread0.393 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2009
Admission routes1
Has abstractyes

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