To catch an obsession: prevalence and predictors of obsession contagion in individuals with obsessive-compulsive disorder
Bibliographic record
Abstract
For decades, emotional contagion literature has provided evidence that everyday emotions such as fear, anger, disgust, sadness, and joy may transfer from one individual to another. Although previous research has explored contagion of psychological conditions such as depression and anxiety, the spread of obsessions in the context of obsessive-compulsive disorder (OCD) has yet to be examined. This thesis aimed to delineate a potential "obsession contagion" by determining the (1) prevalence and characteristics, (2) potential predictors, and (3) prominent emotional experience in individuals with self-reported OCD. Online survey data was analyzed from 125 adults living in Canada or the United States. The vast majority (85%) reported experiencing obsession contagion in their lifetime, significantly more in-person than online, with an average frequency of twice per month. Regression analyses revealed that susceptibility to obsession contagion was significantly associated with higher participant age, elevated emotional contagion, and lower empathy, with these three variables accounting for approximately half of the variance in obsession contagion total scores. While somatic and aggressive obsessions were particularly "contagious", fear and guilt emerged as the most endorsed emotions in self-reported obsession contagion examples. Clinical implications are discussed, including important considerations for advocacy, assessment, and treatment, as well as the need for future qualitative investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".