Risky decision-making and nonsuicidal self-injury among university students: Examining the role of criticism feedback
Bibliographic record
Abstract
Risky decision-making putatively contributes to nonsuicidal self-injury (NSSI) yet empirical support for this association is inconsistent. Studies have not simulated socioemotional contexts most closely linked to NSSI, which may partially explain mixed findings. Accordingly, we examined the association between NSSI and risky decision-making following the receipt of criticism from a close other, a key interpersonal context. The study included 286 university students (Mage = 21.11) oversampled for a lifetime history of NSSI. Participants completed a modified Iowa Gambling Task, in which they chose to play or pass on "good" and "bad" decks associated with monetary gains and losses. Participants also completed the Criticism Gambling Task, which was identical to the Iowa Gambling Task except critical audio comments preceded each block of trials. Based on results of multilevel growth curve analyses, decreases in risky decision-making were steeper on the Iowa Gambling Task compared to the Criticism Gambling Task, suggesting poorer learning in the context of criticism. Further, how past-month NSSI was related to changes in risky decision-making across blocks differed between the two tasks, b = -0.004, t(3140.00) = 2.48, p = .013. On the Iowa Gambling Task, all participants decreased their risky decision-making, whereas on the Criticism Gambling Task, higher past-month NSSI frequencies were associated with riskier decision-making. Our findings support associations between risky decision-making and NSSI in negative socioemotional contexts, consistent with functional models of NSSI.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".