Adverse Childhood Experiences, Distress Tolerance, and Non-Suicidal Self-Injury: A Cross-Sectional Mediation Model Among College Students from Six Countries
Bibliographic record
Abstract
Non-suicidal self-injury is defined as the deliberate, direct, and socially unacceptable destruction of body tissue without suicidal intent. Global estimates have shown high onset and frequency of NSSI in adolescents and is considered to be an issue of significant cross-national prevalence. Adverse childhood experiences (ACEs) have displayed strong empirical linkages to NSSI; however, more research identifying mechanisms linking ACEs to NSSI is needed. The present study examined the associations between ACEs, distress tolerance (DT), and NSSI severity among college students from six countries. Specifically, we examined whether ACEs predict past year NSSI severity via distress tolerance. Participants were 811 college students (78.4% female) from six countries (USA, Argentina, Spain, South Africa, England, Canada) who endorsed past year NSSI and completed study measures. Within our estimated model, we found that higher ACEs scores were associated with lower DT, which in turn was associated with greater NSSI severity (indirect β = .03, 99% CIs=0.01, 0.06). When accounting for DT, higher ACEs scores were still associated with greater NSSI severity (direct β = .23, 99% CIs = 0.14, 0.31). When testing for model invariance, we found that all effects were consistent across country and sex at birth groupings. These findings suggest that introducing and strengthening healthy coping mechanisms may provide emerging adults who experienced ACEs with adaptive strategies to manage distress and may reduce incidence of NSSI as a result.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".