‘Things that shouldn’t be’: a qualitative investigation of violation-related appraisals in individuals with OCD and/or trauma histories
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".