The reasons dissociative disorder patients self-injure
Bibliographic record
Abstract
Background: Most individuals with dissociative disorders (DDs) report engaging in self-injury. Objective: The present study aimed to understand the reasons for self-injury among a clinical sample of 156 DD patients enrolled in the TOP DD Network study. Method: Participants answered questions about self-injury, including a prompt asking how often they are aware of the reasons they have urges to self-injure, as well as a prompt asking them to list three reasons they self-injure. Results: Six themes of reasons for self-injury, each with subthemes, were identified in the qualitative data: (1) Trauma-related Cues, (2) Emotion Dysregulation, (3) Stressors, (4) Psychiatric and Physical Health Symptoms, (5) Dissociative Experiences, and (6) Ineffective Coping Attempts. Participants reported that they were able to identify their reasons for self-injuring sometimes (60.26%) or almost always (28.85%), with only 3.20% unable to identify any reasons for their self-injury. Conclusion: Results suggest that the vast majority of DD patients (92.31%) reported being at least partially unaware of what leads them to have self-injury urges, and many individuals with DDs experience some reasons for self-injury that are different from those with other disorders. The treatment implications of these findings are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".