The relationship between obsessions and the self: Feared and actual self‐descriptions in a clinical obsessive–compulsive disorder sample
Bibliographic record
Abstract
Cognitive models of obsessive-compulsive disorder (OCD) posit the relevance of the self in OCD, although the nature of this association is still unclear. We aimed to explore actual and feared selves and its association with obsessions and intrusions in a group of OCD patients. A group of 58 patients with OCD identified their most upsetting obsession and intrusion (non-clinical obsession) experienced in the past 3 months. These cognitions were classified as either moral-based or autogenous (obsessions n = 32; intrusions n = 26) or non-moral-based or reactive, depending on their content. Next, patients described their actual self and their feared self, that is, the person they feared being or becoming, and whether they believed these descriptions were associated with their obsessions/intrusions. Results indicate that individuals with OCD described themselves as insecure, anxious and fearful, but also as good and nice. They particularly feared a selfish, aggressive, bad, liar, coward, insecure and arrogant self. Two-thirds of the patients believed that their obsessions said something about their actual self (65.52%) and that their obsessions brought them closer to the person they do not want to be (62.06%). A third of patients believed their intrusions said something about their actual self (actual self: 30.35%; feared self: 25%), which was a significantly lower percentage than for obsessions. These associations existed independent from the content of the obsession and/or intrusion, although patients with obsessions with moral-based contents more often tended to believe that their obsessions brought them closer to the person they do not want to be. Results suggest the relevance of the real and feared selves in the maintenance of obsessions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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".