Learning from experience: Within‐ and between‐person associations of the consequences, frequency, and versatility of nonsuicidal self‐injury
Bibliographic record
Abstract
OBJECTIVE: Behavioral models of nonsuicidal self-injury (NSSI) propose that experiencing desirable consequences following NSSI reinforces the behavior. However, these models do not specify whether experiencing more desirable consequences relative to other people (between-person), an individual's own average (within-person), or both, predicts NSSI severity. To address this gap, this study investigated the prospective, within- and between-person associations of desirable NSSI consequences with NSSI frequency (number of episodes) and versatility (number of methods). METHODS: = 22.95) with a history of NSSI completed online surveys assessing NSSI consequences, frequency, and versatility every three months for one year. RESULTS: Within-person increases in desirable emotional consequences were unrelated to NSSI frequency three months later but predicted increases in NSSI versatility. Within-person increases in desirable social consequences predicted decreases in NSSI frequency three months later but were unrelated to NSSI versatility. Between-person variability in desirable consequences was unrelated to NSSI severity. CONCLUSIONS: Findings were partially consistent with behavioral models of NSSI. Going forward, we recommend that: (1) behavioral models articulate the salience of within-person fluctuations in consequences; (2) research clarifies the role of social consequences; and (3) clinicians use repeated assessments of emotional consequences to identify periods of elevated NSSI risk.
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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 | 0.002 | 0.017 |
| 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 | 0.000 | 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".