Examining predictors of non-suicidal self-injury among college students: A prospective cohort study
Bibliographic record
Abstract
Introduction Non-suicidal self-injury (NSSI) refers to the deliberate act physical harm without intent of suicide. Common forms of NSSI include cutting, burning, and hitting oneself. The prevalence of NSSI among college students have been estimated to be between 15% to 25%.and higher than in the general population. Investigating patterns associated neurobiological and personality traits may provide a more comprehensive understanding of NSSI. Objectives We aim to identify latent trajectory classes for NSSI behavior among college students. We expect that baseline personality and the behavioral inhibition/activation scales (BIS/BAS) will predict NSSI trajectory. Methods A total of 704 first-year university students at University of Victoria, Canada, were recruited in the beginning of the first semester over two consecutive academic years. Participants attended a baseline testing session completing self-report measures including the Ten Item Personality Inventory, BIS/BAS and NSSI instruments. There were monthly follow-up sessions from October to April. Longitudinal data will be analysed with latent growth curve modeling and group-based trajectory modeling, and baseline predictors will be analysed with multivariate logistic regression. Results Latent class growth analysis found three distinct classes of NSSI during the follow-up period. A small percentage (2.4%) of the participants had a high degree of self-injury throughout the follow-up period. A second class of 13.4% of the participants had a moderate degree of self-injury at baseline, which fell throughout the follow-up period. Lastly, a third class of the majority of the participants (84.3%) had minor or none self-injury both at baseline and in the follow-up period. Concerning baseline predictors, higher openness and BAS drive were associated with lower NSSI at baseline. Conclusions In line with previous studies, we identified three distinct trajectories of NSSI behavior among college students. Notably, low openness and low BAS drive were associated with a degree of NSSI at baseline. These findings suggest that openness and drive may play a protective role in NSSI, providing valuable insights for future prevention and intervention efforts. The project is part of the Collaborative Research Program at the International Society for the Study of Self-Injury. Disclosure of Interest None Declared
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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 | 0.002 |
| 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 | 0.000 |
| 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 | 0.001 |
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".