A preliminary investigation of the relationships between attachment insecurity, fear of compassion, and <scp>OCD</scp> severity
Bibliographic record
Abstract
OBJECTIVES: The most successful psychological treatment for obsessive-compulsive disorder (OCD) is cognitive behavioural therapy (CBT). However, treatment success remains around 50% when refusal and dropout are considered. Purdon (Journal of Behavior Therapy and Experimental, Psychiatry, 2023, 78, 101773) argued that the CBT model is under-specified, suggesting that there may be important treatment targets that are not directly addressed. Based on emerging research, she identified insecure attachment and fear of compassion (FOC) as potentially important targets. Insecure attachment and FOC are associated with OCD symptoms, and past research suggests that FOC may explain the relationship between attachment insecurity and emotional distress. We reasoned that FOC may also be an important predictor of OCD symptom severity. METHODS: We conducted two preliminary, pre-registered online survey studies with undergraduate samples to explore potential theoretical relationships between attachment, FOC, and OCD. RESULTS: Study one (N = 329) revealed that the indirect effect of attachment anxiety on OCD symptom severity through fear of self-compassion was significant, even when controlling for trait self-compassion. A significant indirect effect of attachment avoidance predicting OCD severity, through fear of receiving compassion, also emerged. Study two (N = 340) replicated these findings and extended this research by controlling for depression. CONCLUSIONS: Taken together, these findings suggest that FOC could be an important variable to consider when conceptualizing OCD. Further exploration is warranted to understand the directionality of these relationships and whether attachment and FOC could be valuable targets in OCD treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".