Examining the association between exposure to major stressful life events and non-suicidal self-injury
Bibliographic record
Abstract
Experiencing major stressful life events (SLEs) may contribute to risk for non-suicidal self-injury (NSSI). Prior research has established that a higher number of SLEs is linked with more frequent NSSI episodes among young adults. However, life stress is typically assessed with checklist approaches; these methods introduce frequent idiosyncratic misinterpretations of stress categories (e.g., major illness). Coupled with the unclear boundary of what constitutes SLEs, relying on participants’ subjective appraisals of stress severity attenuates validity. Specifically, individuals with a history of NSSI may have certain cognitive characteristics that contribute to an over-appraisal of event severity. Interview-based, contextual life stress systems overcome these limitations by having third-party raters blind to participant characteristics determine stress severity based on a standardized set of circumstances. Accordingly, this study used the Life Events and Difficulties Schedule 2 (LEDS-II), which is a gold-standard, semi-structured life stress interview and rating system. Specifically, we focused on the cumulative severity of episodic stressors (i.e., SLEs whose major features occur within a 2-week period). In preliminary analyses of 53 participants (Mage= 20.17, SDage= 2.51), cumulative stress severity (M =2.96, SD =3.20) was not significantly associated with the number of NSSI episodes participants reported in the past year (M = 22.75, SD = 67.46), r(53) = .13 p = .370. Data collection is ongoing, and we anticipate adding 30 participants (total n=83) to the analyses. Current findings do not align with previous literature on the association between SLEs and NSSI. However, they provide a methodological foundation for how to improve future research on SLEs and NSSI in young adults through the application of gold-standard life stress measures.
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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 | 0.002 | 0.007 |
| 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) | 0.003 | 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".