Childhood trauma in obsessive compulsive disorder: The roles of alexithymia and attachment
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the interrelationships between childhood trauma, attachment, alexithymia, and the severity of obsessive compulsive disorder (OCD) in a cohort of participants with OCD. RATIONALE: There is a growing body of research linking traumatic experiences in childhood with the development of OCD. The mechanisms involved in this association are not yet clear. METHODS: The sample was comprised of 82 people with OCD and 92 comparison participants. A cross-sectional design was used, utilizing internet-mediated administration of the Childhood Trauma Questionnaire - revised (CTQ-R); the Yale-Brown Obsessive Compulsive Scale - Self-Report (Y-BOCS-SR); the Experiences in Close Relationships Scale (ECR); and Toronto Alexithymia Scale (TAS-20). Partial least squares (PLS) analysis was used to determine significant paths between the constructs. RESULTS: Results of PLS analysis supported all of the hypotheses made: there was a significant positive correlation between childhood trauma and attachment avoidance, which in turn was significantly positively associated with alexithymia. Alexithymia was significantly associated with the severity of OCD symptoms and the number of OCD symptoms. Mediational analysis showed that alexithymia significantly carried an influence from attachment avoidance to the severity of obsessions and the number of obsession symptoms. CONCLUSIONS: There is a relationship between childhood trauma and OCD, however this relationship is not direct in nature but is influenced by peoples' past experiences with significant others and associated difficulties in emotional processing.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".