Exploring the association of deliberate self-harm with emotional relief using a novel Implicit Association Test.
Bibliographic record
Abstract
Despite the growing consensus that negative reinforcement in the form of emotional relief plays a key role in the maintenance of deliberate self-harm (DSH), most of the research in this area has relied exclusively on self-report measures of the perceived motives for and emotional consequences of DSH. Thus, the primary aim of this study was to extend extant research on the role of emotional relief in DSH by examining the strength of the association of DSH with emotional relief using a novel version of the Implicit Association Test (IAT). The strength of the DSH-relief association among both participants with (vs. without) DSH and self-harming participants with (vs. without) BPD, as well as its associations with relevant clinical constructs (including DSH characteristics, self-reported motives for DSH, BPD pathology, and emotion dysregulation and avoidance) were examined in a community sample of young adults (113 with recent recurrent DSH; 135 without DSH). As hypothesized, results revealed stronger associations between DSH and relief among participants with versus without DSH, as well as among DSH participants with versus without BPD. Moreover, the strength of the DSH-relief association was positively associated with DSH frequency and versatility (both lifetime and at 6-month follow-up), BPD pathology, emotion dysregulation, experiential avoidance, and self-reported emotion relief motives for DSH. Findings provide support for theories emphasizing the role of emotional relief in DSH (particularly among individuals with BPD), as well as the construct validity, predictive utility, and incremental validity (relative to self-reported emotion relief motives) of this IAT.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".