When the Problem is Beneath the Surface in OCD: The Cognitive Treatment of a Case of Pure Mental Contamination
Bibliographic record
Abstract
BACKGROUND: Mental contamination is a phenomenon whereby people experience feelings of contamination from a non-physical contaminant. Rachman (2006) proposes that standard cognitive behavioural treatments (CBT) need to be adapted here and there is a developing empirical grounding supporting the concept, although suggestions on adapting treatment have yet to be tested. METHOD: A single case study is presented of a man with a 20-year history of severe treatment resistant Obsessive Compulsive Disorder (OCD) characterized by mental contamination following the experience of "betrayal". He was offered two consecutive treatments: standard CBT and then (following disengagement with this) a cognitive therapy variant adapted for mental contamination. Clinician and patient rated OCD severity was measured at baseline and the start and end of both interventions. RESULTS: Six sessions of high quality CBT were initially attended before refusal to engage with further sessions. There were no changes in OCD severity ratings across these sessions. A second course of cognitive therapy adapted for mental contamination was then offered and all 14 sessions and follow-ups were attended. OCD severity fell from the severe to non-clinical range across these sessions. CONCLUSIONS: The need to consider adapting standard treatments for mental contamination is suggested. Limitations and implications are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".