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Exposure-Based Treatment for Obsessive Compulsive Disorder

2012· book· en· W350341042 on OpenAlexaff
Jonathan S. Abramowitz, Steven Taylor, Dean McKay

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialPsychologyOutcome (game theory)Exposure and response preventionPsychological interventionPsychotherapistObsessive compulsiveClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Exposure and response prevention (ERP) is one of the oldest and most effective treatments for obsessive compulsive disorder. The present chapter describes the empirical foundations, development, delivery, and latest research on ERP. Commonly used methods and procedural variants of ERP are described, along with findings concerning the underlying mechanisms of action. The efficacy of ERP in relation to other treatments is discussed, in addition to research on the long-term effects of ERP and its effects in non-research settings. Pretreatment predictors of the outcome of treatments using ERP are also considered. Efforts to improve treatment outcome are discussed, including research into the benefits of combining ERP with other psychosocial interventions such as cognitive therapy, or with particular medications. The chapter concludes by considering important future research directions for improving the outcome of treatment packages that include ERP.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.264
Teacher spread0.236 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2012
Admission routes1
Has abstractyes

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