The role of interpersonal conflict and perceived social support in nonsuicidal self-injury in daily life.
Bibliographic record
Abstract
Although accumulating microlongitudinal research has examined emotion regulatory models of nonsuicidal self-injury (NSSI), few studies have examined how interpersonal contingencies influence daily NSSI behavior. Participants with repeated NSSI (N = 60) provided daily ratings of perceived social support, interpersonal conflict, and NSSI urges and behaviors for 14 days. Consistent with interpersonal models of NSSI, we hypothesized that participants would be more likely to engage in NSSI on days when they experienced high levels of interpersonal conflict, that NSSI acts that were revealed to others would be followed by desirable interpersonal changes (i.e., greater support, less conflict), and that these interpersonal changes would, in turn, predict stronger NSSI urges and more frequent NSSI behavior. Consistent with hypotheses, daily conflict was associated with stronger same-day NSSI urges and greater likelihood of NSSI acts. Perceived support increased following NSSI acts that had been revealed to others, but not unrevealed NSSI acts. This perceived support was, in turn, associated with a stronger NSSI urges and greater likelihood of engaging in NSSI on the following day. Moreover, participants whose NSSI was revealed to others engaged in more total NSSI acts during the diary period than those whose NSSI was not revealed to others. Inconsistent with hypotheses, interpersonal conflict did not decrease following NSSI, regardless of whether or not these acts were revealed to others. Together, these results provide preliminary support for interpersonal reinforcement models of NSSI and highlight the importance of expanding research in this area to include interpersonal contingencies that may influence this behavior. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".