A Longitudinal Examination of the Predictive Effects of Alexithymia on Nonsuicidal Self‐Injury Among Emerging Adults
Bibliographic record
Abstract
OBJECTIVES: Emerging evidence suggests that alexithymia, a psychological construct defined by the inability to describe emotion, differentiate feelings, and think in an internally oriented way, may be relevant in understanding engagement in nonsuicidal self-injury (NSSI). However, there is a paucity of longitudinal work on alexithymia and NSSI, which is necessary to discern whether alexithymia may heighten risk for NSSI over time. METHODS: In the present study, the association between alexithymia and NSSI was examined among 1125 emerging adults (Mage = 17.96, 72% female), who completed a survey at two time points 4 months apart. RESULTS: Participants with a history of NSSI reported higher levels of alexithymia than those with no NSSI engagement. A zero-inflated negative binomial regression model revealed that higher alexithymia at Time 1 predicted greater diversity in NSSI methods (i.e., NSSI versatility), but not NSSI frequency, at Time 2, for those already engaging in NSSI (p < 0.01, controlling for NSSI history at Time 1, emotion regulation difficulties, age, and gender). Significant differences were found in NSSI functions based on alexithymia among individuals with a lifetime history of NSSI at both time points. Among participants with a history of NSSI, alexithymia was most strongly correlated with anti-dissociation, sensation seeking, self-punishment, toughness, and interpersonal boundaries NSSI functions. CONCLUSION: Findings underscore that alexithymia may be relevant to understanding NSSI severity.
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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 | 0.001 | 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) | 0.002 | 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".