Different <scp>self‐damaging</scp> behaviours, similar motives? Testing measurement invariance of motives for nonsuicidal <scp>self‐injury</scp> , disordered eating and substance misuse
Bibliographic record
Abstract
OBJECTIVES: Theory and research suggest that distinct self-damaging behaviours (SDBs; e.g., nonsuicidal self-injury [NSSI], restrictive eating, binge eating, drug misuse, alcohol misuse) share similar motives. However, few studies have used a common self-report inventory to investigate the shared relevance and relative salience of motives for SDBs. Accordingly, the present study: (1) examined whether self-report scales assessing intrapersonal motives (i.e., relieving negative emotions, enhancing positive emotions, punishing oneself) and interpersonal motives (i.e., bonding with others, conforming with others, communicating distress, communicating strength, reducing demands) have invariant factor structures across SDBs; and (2) compared the salience of these motives across SDBs. METHODS: = 35.41 years) with a history of SDBs were allocated to the following groups: NSSI (n = 213), restrictive eating (n = 200), binge eating (n = 200), drug misuse (n = 200) or alcohol misuse (n = 205). Participants reported on their motives for engaging in their allocated SDB. Measurement invariance analyses compared the factor structures and latent means of the motive scales across SDBs. RESULTS: The motive scales had comparable factor structures across SDBs. Intrapersonal motives were most strongly endorsed for NSSI and drug misuse. Interpersonal motives were most strongly endorsed for drug and alcohol misuse. All motives were least salient to restrictive eating. CONCLUSIONS: Results suggest that common motives underlie distinct SDBs and that they can be adequately assessed using a single self-report inventory. However, certain motives are more relevant to some SDBs than others, with restrictive eating being the most motivationally distinct SDB. This knowledge can inform transdiagnostic models and interventions for SDBs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".