Rethinking Unacceptable Thoughts: Validation of an Expanded Version of the Dimensional Obsessive-Compulsive Scale (DOCS)
Bibliographic record
Abstract
The Dimensional Obsessive-Compulsive Scale (DOCS) is widely used to measure obsessive-compulsive disorder (OCD) severity across four broad symptom dimensions (i.e., contamination, responsibility for harm, unacceptable thoughts, symmetry). Despite its proven utility, there is reason to suspect that the unacceptable thoughts subscale conflates different types of unacceptable thoughts that are meaningfully distinct from one another. In the current study, we first evaluated the psychometric properties of a newly developed DOCS violent and/or aggressive thoughts subscale. We then examined the factor structure, psychometric properties, and diagnostic sensitivity of a seven-factor version of the DOCS that includes the four original DOCS subscales and three more-specific versions of the unacceptable thoughts scale (i.e., sexually intrusive thoughts, violent and/or aggressive thoughts, and scrupulous or religious thoughts). The sample included 329 residential and intensive outpatients, the majority of which had a diagnosis of OCD (75.2%). The new unacceptable thoughts subscales demonstrated convergent and discriminant validity with unique associations between the subscales and depression, suicide, and perceived threat from emotions that were not present in the broader unacceptable thoughts subscale. The seven-factor version of the DOCS demonstrated slightly lower levels of diagnostic sensitivity than the original DOCS. Thus, the four-factor version of the DOCS is recommended for screening purposes. A score of 40 of higher on the seven-factor version of the DOCS best predicted a diagnosis of OCD. Overall, the three additional unacceptable thoughts subscales appear to be distinct dimensional categories that have potential value in research and clinical settings.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".