Compensatory self-injury: Posttraumatic stress, depression, and the role of dissociation.
Bibliographic record
Abstract
OBJECTIVE: Despite a number of studies, the reasons for self-injurious behavior (SIB) have yet to be clearly specified. Using path analysis, we sought to test the hypothesis that exposure to adverse events produces depression and posttraumatic stress, which in turn motivate dissociation that, when at high levels, supports the use of SIB. METHOD: A sample of 679 adults (54% female, mean age = 53 years) were recruited from the general population by a national survey company, and administered measures evaluating posttraumatic stress, depression, dissociation, and SIB. RESULTS: A total of 4.3% of participants reported some level of SIB within the prior 6 months. Younger age, exposure to adverse events, posttraumatic stress, depression, and dissociation were all related to SIB by univariate analyses. Path analyses revealed that although adverse events predicted posttraumatic stress and depression, which were then associated with SIB, these paths to SIB were no longer significant once dissociation was entered into the model, indicating full mediation. CONCLUSION: Rather than arising directly from posttraumatic stress or depression, SIB may occur most proximally in response to dissociation, with the pain associated with SIB potentially serving to interrupt or titrate unwanted hypoarousal and numbing. Clinicians should consider specifically targeting dissociation and its adversity-related antecedents when treating SIB. (PsycINFO Database Record
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".