It's the Feeling Inside My Head: A Qualitative Analysis of Mental Contamination in Obsessive-Compulsive Disorder
Bibliographic record
Abstract
BACKGROUND: It was recently proposed that feelings of dirtiness and pollution can arise in the absence of physical contact with a contaminant. At present, there is limited data regarding the qualitative features of this construct of "mental contamination", although it is hypothesized to be particularly relevant to Obsessive Compulsive Disorder (OCD), where compulsive washing in response to contamination fear is a common symptom presentation (Rachman, 2006). AIMS AND METHOD: The aim of this research was to explore the qualitative features of mental contamination in 20 people with contamination-based OCD, using a semi-structured interview. RESULTS: All participants reported times when they had felt dirty or contaminated in the absence of physical contact with a dirty or dangerous object. Mental contamination generated diffuse feelings of internal dirtiness not localized to the hands, which evoked urges to wash (100% participants), neutralize (80% participants) and avoid (85% participants). CONCLUSIONS: In support of the theory outlined by Rachman (2006), mental contamination was found to take a number of forms, be primarily associated with a human source, generate internal dirtiness and cause emotional distress and urge to wash. The clinical implications of these findings are discussed and ideas for future research are proposed.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".