Influence of perceived peer behavior on engagement in self‐damaging behaviors during the transition to university
Bibliographic record
Abstract
As students transition to university, they experience significant social changes that can affect their behaviors, including self-damaging behaviors like disordered eating, problematic alcohol/drug use, suicidal thoughts, and non-suicidal self-injury (NSSI). Building on prior work, we examined the associations between (1) perceptions of peers' engagement in self-damaging behaviors predicting one's own subsequent engagement in such behaviors (i.e., socialization) and (2) one's own engagement in self-damaging behaviors predicting perceptions of peers' subsequent engagement in such behaviors (i.e., selection). We also examined whether these associations were moderated by the source of influence (close peer/acquaintance) and degree of social disconnection experienced by the student. First-year university students (N = 704) were asked to complete seven monthly surveys. Multilevel models indicated that when students perceived their close peers had engaged in NSSI or suicidal thinking, they had seven times greater odds of future engagement in the same behavior, implying that socialization increases the risk of these behaviors among university students. Perception of acquaintances' NSSI also predicted greater odds of a student's own NSSI the following month. Social disconnection increased the likelihood of matching own behaviors to perceptions of acquaintances' alcohol abuse, highlighting the importance of fostering connections/mentors to reduce self-damaging behaviors on college campuses. Furthermore, when students engaged in alcohol abuse, they had almost four times greater odds of reporting that their acquaintances abused alcohol the following month, emphasizing the importance of the wider social network in alcohol use behaviors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".