Negative and Positive Emotional Reactivity in Women With and Without a History of Self-Injury
Bibliographic record
Abstract
In trying to better understand why certain individuals self-injure, researchers have proposed high emotional reactivity for negative emotions may influence vulnerabilities and predispose individuals to react to stressful situations in a dysregulated manner, thus engaging in non-suicidal self-injury (NSSI). However, the role of emotional reactivity for positive emotions in those with a history of NSSI is still unclear. Thus, the present study sought to examine group differences in the reactivity of (a) negative and (b) positive emotions in young adults with and without a history of NSSI engagement, and (c) to evaluate whether the reactivity of positive emotions could predict NSSI engagement when controlling for reactivity of negative emotions. The sample consisted of 96 female students who reported engaging in NSSI within the past 2 years ( M age = 20.28 years, SD = 1.65) and an age-matched female comparison group with no NSSI history ( M age = 20.43 years, SD = 1.76). Results from separate MANOVAs indicated individuals with a history of NSSI reported higher negative reactivity across all aspects (emotional intensity, sensitivity, and persistence) than the comparison group, Wilk’s λ = .86, F (3,188) = 10.65, p < .001, partial η 2 = .145; however, no significant differences emerged for positive reactivity, Wilk’s λ = .99, F (3,188) = 0.52, p = .669. Moreover, a logistic regression revealed that persistence of negative emotions was the only significant predictor of NSSI, Wald χ 2 (1) = 4.54, p = .03. The present results highlight the importance of the persistence of negative emotions for individuals who engage in NSSI. Furthermore, the current study provides the first suggestion of no significant differences in positive emotional reactivity between individuals with and without NSSI; underlining the importance of focusing on negative emotional reactivity in clinical practice as well as using positive emotions to “undo” the effect of negative emotions.
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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 | 0.000 | 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 | 0.000 |
| 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".