A longitudinal examination of predictors of nonsuicidal self‐injury disclosures among university students
Bibliographic record
Abstract
OBJECTIVE: There is a paucity of longitudinal research on predictors of disclosures of nonsuicidal self-injury (NSSI) among emerging adults. However, understanding the factors that facilitate disclosure is critical, as disclosure may serve as a first step in accessing care. To address this gap, the present study examined predictors of prospective NSSI disclosures in a postsecondary student sample. METHODS: = 17.96; 74.9% women) reported on several potential predictors of NSSI disclosure, and their disclosure history at baseline and 4- and 8-month follow-ups. RESULTS: It was found that 22% of students reported disclosing NSSI during the first year of university; students who had previously disclosed, and who reported greater NSSI severity, were more likely to disclose over time. CONCLUSION: Results of the present study suggest that disclosures often occur in the postsecondary context, and students who disclose NSSI may engage in more severe NSSI behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".