WHY ARE CERTAIN INTRUSIVE THOUGHTS MORE UPSETTING THAN OTHERS?
Bibliographic record
Abstract
Recent cognitive behavioural models of obsessive compulsive disorder (OCD) suggest that the misinterpretation of the meaning of intrusive thoughts plays a pivotal role in the escalation of these thoughts to clinical obsessions, but less attention has been paid to why only certain intrusive thoughts become the focus of these misappraisals. Theoretical speculation suggests that thoughts that have relevance for an individual's value system or sense of self may be particularly salient and upsetting for people. The role of thought appraisal and contradiction of valued aspects of self were examined in a nonclinical population. It was hypothesized that participants reporting on upsetting intrusive thoughts would appraise these thoughts negatively and would report that these thoughts contradict important aspects of self to a greater degree than participants reporting on less upsetting intrusive thoughts. Participants (N = 64) were randomly assigned to report on either the most or least upsetting intrusive thought they had experienced. They completed questionnaires on appraisals of these thoughts, valued aspects of self, and contradiction of self. Consistent with predictions, participants reporting on more upsetting thoughts appraised these thoughts in a more negative manner and reported that these thoughts contradicted valued aspects of self to a greater degree than participants in the least upsetting thought group. These results support Salkovskis' (1985) and Rachman's (1997, 1998) cognitive behavioural models of OCD, and suggest that the degree of contradiction of self may help us understand why some obsessional thoughts are much more upsetting than others.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".