Investigating the Potential Contributors to Non-Suicidal Self-Injury: Stress, Emotional Dysregulation, and Alexithymia
Bibliographic record
Abstract
Research has investigated perceived stress, non-suicidal self-injury (NSSI), emotional dysregulation, and alexithymia independently. However, there have been no studies to date that have simultaneously examined the relationship among all these variables. The purpose of this study was to examine these variables among those who had or had not engaged in NSSI. Five hundred seventy-seven college students completed self report measures of non-suicidal self-injury, perceived stress level, emotional dysregulation, and alexithymia. As hypothesized, those who had engaged in self-harm behaviors reported higher levels of perceived stress, emotional dysregulation, and alexithymia. There were no gender differences except on perceived stress levels, where women reported a greater level of stress than men. As hypothesized, there was a significant positive correlation between alexithymia and perceived stress. Contrary to the hypothesis, the emotional dysregulation and alexithymia scales were strongly correlated. Lastly, emotional dysregulation was found to be the best predictor of group classification (had ever engaged in self-harm or not), followed by perceived stress, and lastly alexithymia. Further 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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".