Depressive Symptoms, Perceived Stress, Self-Compassion and Nonsuicidal Self-Injury Among Emerging Adults: An Examination of the Between and Within-Person Associations Over Time
Bibliographic record
Abstract
Many emerging adults report experiencing mental health challenges (e.g., depressive symptoms and perceived stress) during the transition to university. These mental health challenges often coincide with increased engagement in nonsuicidal self-injury (NSSI; e.g., self-cutting or burning without lethal intent), but longitudinal research exploring the nature of the associations among depressive symptoms, perceived stress, and NSSI are lacking. In the present study, it was examined whether depressive symptoms and perceived stress predicted increased risk for NSSI over time (or the reverse), and whether these effects were mediated or moderated by self-compassion. The sample consisted of 1,125 university students ( Mage = 17.96 years, 74% female), who completed an online survey three times in first year university. A random intercept cross-lagged panel model revealed that higher depressive symptoms, perceived stress, NSSI, and lower self-compassion often co-occurred, but only NSSI predicted increased perceived stress over time. Theoretical and practical implications 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".