Suicidality in People With Obsessive-Compulsive Symptoms or Personality Traits
Bibliographic record
Abstract
Objective: Obsessive-Compulsive Disorder (OCD) and Obsessive-Compulsive Personality Disorder (OCPD) have been reported to be associated with mood instability (MI), depression and the spectrum of suicidality. We examined whether OCD and OCPD, and obsessional thoughts of death, are associated with suicidal thoughts, non-suicidal self-injury and attempted suicide. Methods: We used data from the 2000 British Adult Psychiatric Morbidity survey that elicited symptoms of OCD with a computerized version of the CIS-R and traits of OCPD with a self-completed version of the SCID-II. We created a series of logistic regression models, first entering only OCD and OCPD traits in separate models, to which depression and mood instability (MI) were added. We also examined the relation of obsessional thoughts about death with self-harm in a network analysis model that included the main symptoms of mood instability and depression. Results: Both OCD and OCPD were significantly associated with suicidal thoughts, and suicidal actions. Only OCPD was associated with non-suicidal self-injury. Depression and MI are both predictors of all three self-harm outcomes. In the network analysis, MI bridged depressive symptoms with self-harm. Obsessions and compulsions connected to self-harm through MI. Conclusions: The total burden of OCD and OCPD is responsible for the association of OCD and OCPD and suicidal thoughts, rather than thoughts of death or harm alone. The association of MI and depression with thoughts and actions of self-harm is confirmed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".