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Record W4396229738 · doi:10.1017/s1352465824000201

‘Things that shouldn’t be’: a qualitative investigation of violation-related appraisals in individuals with OCD and/or trauma histories

2024· article· en· W4396229738 on OpenAlexaff
Sandra Krause, Adam S. Radomsky

Bibliographic record

VenueBehavioural and Cognitive Psychotherapy · 2024
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsConcordia University
Fundersnot available
KeywordsFeelingPsychologyCognitionIdentification (biology)Social psychologyCognitive psychologyEcologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background: Cognitive models of mental contamination (i.e. feelings of internal dirtiness without contact with a contaminant) propose that these feelings arise when individuals misappraise a violation. However, an operational definition of ‘violation’ and identification of specific violation misappraisals is limited. Aims: This study’s aim was to elaborate on cognitive models using qualitative data from those with lived experience to fill these gaps. Method: Twenty participants with a diagnosis of obsessive-compulsive disorder and/or a trauma history took part in a semi-structured interview about violation. Grounded theory was used to analyse interview transcripts. Discussion: Three categories emerged, each with several themes – qualities of violation , violation-related appraisals , and violation-related behaviours . Different violation-related appraisals were associated with different emotions and urges. Specific self-focused appraisal sub-themes (i.e. permanence of consequences ; self-worth ; responsibility, self-blame and regret ) were most closely related to emotions tied to mental contamination. These findings support and expand upon existing cognitive models of mental contamination, identifying key violation-related appraisals and differentiating between mental contamination-related appraisals and those related to other emotional sequelae. Future quantitative and experimental research can evaluate the potential of these appraisals as intervention targets.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.372
Teacher spread0.312 · 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 designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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