Stressful Experiences in University Predict Non-suicidal Self-Injury Through Emotional Reactivity
Bibliographic record
Abstract
Theoretical perspectives on non-suicidal self-injury (NSSI; direct and deliberate self-injury without lethal intent such as self-cutting or hitting) have long underscored the affective regulating properties of NSSI. Less attention has been given to the processes through which individuals choose to engage in NSSI, specifically, to regulate their distress. In the present study, we tested one theoretical model in which recent stressful experiences facilitates NSSI through emotional reactivity. Further, we tested whether the indirect link between stressful experiences and NSSI was moderated by several NSSI specific risk factors (e.g., having friends who engage in NSSI). Given the widespread prevalence of NSSI among community-based samples of adolescents and emerging adults, we surveyed 1,125 emerging adults in first-year university at a large academic institution (72% female, Mage = 17.96, 25% with a recent history of NSSI at Time 1). Participants completed an online survey three times (assessments were 4 months apart), reporting on their recent stressful experiences in university, emotional reactivity, NSSI, as well as three NSSI specific risk factors (i.e., close friend engagement in NSSI, high self-disgust, and low fear of pain). As expected, path analysis revealed that there was a significant indirect effect of recent stressful experiences on NSSI engagement, through emotional reactivity. However, this effect was maintained across moderator analyses. These novel findings underscore the salient role of proximally occurring stressors in the prediction of NSSI among emerging adults in university, and can inform developing theoretical perspectives on NSSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".