Does higher‐than‐usual stress predict nonsuicidal self‐injury? Evidence from two prospective studies in adolescent and emerging adult females
Bibliographic record
Abstract
BACKGROUND: Nonsuicidal self-injury (NSSI) is highly prevalent among adolescent and emerging adult females. Most studies examining the relationship between stress and NSSI largely have relied on aggregate self-report measures of stress and between-person models. Using data from two prospective samples, this manuscript tests the hypothesis that within-person models of NSSI provide better clinical markers of risk for NSSI than between-person models of NSSI. METHODS: Two samples (Sample 1: 220 high-risk girls, M age = 14.68, SD = 1.36, baseline assessment and 3-month follow-ups for 18 months; Sample 2: 40 emerging adult females with a history of NSSI, M age = 21.55, SD = 2.14, 14 days with daily retrospective reports) were followed prospectively and completed validated measures of stress and NSSI. Models were adjusted for age and depression. RESULTS: In Sample 1, a within-person model demonstrated that higher-than-usual (but not average) stress levels predicted NSSI within the same 3-month wave. In Sample 2, results from a within-person model with daily diary assessment data showed that higher-than-usual stress (but not average daily stress) predicted same-day NSSI. CONCLUSIONS: Together, our results suggest that higher-than-usual stress, relative to one's typical stress level, but not average stress levels, signals times of enhanced risk for NSSI. These results highlight the clinical utility of repeated assessments of stress.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".