A Multidimensional Examination of Dissociation and Its Relationship With Suicide Ideation and Attempts
Bibliographic record
Abstract
• Used multiple measures to capture distinct dimensions of dissociation. • Suicide ideation was most strongly linked to Depersonalization and Zoning Out. • Suicide attempts (among ideators) were most strongly linked to Reexperiencing. • Psychological pain and PTSD may partly explain the dissociation–ideation links. • PTSD may explain the association between Reexperiencing and suicide attempts. Dissociation is a multidimensional construct linked to suicide risk. However, it is unclear which dimensions of dissociation are most important to suicide risk, and whether they correlate with suicide ideation (SI), suicide attempts (SA), or both. Thus, the present study utilized multiple measures of dissociation to examine the differential relationships of distinct dissociation dimensions to SI and SA. Participants were 545 undergraduates, oversampled for suicide risk, including 141 with histories of SA and 255 with histories of SI but not attempts. Suicide history, dissociative symptoms, and potential covariates were measured via validated self-report questionnaires. In general, analyses indicated that dissociation was associated with both SI and SA among those with ideation. Regarding specific dimensions, Depersonalization/derealization and Zoning out best predicted SI, whereas Reexperiencing best distinguished SA from those with SI only. The findings suggest that different types of dissociative experiences are associated with different aspects of suicide risk. Future research should continue to utilize updated, multidimensional models of dissociation to better understand the role of this construct in suicide risk.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".