Emotional difficulties mediate the impact of adverse childhood experiences on compulsive buying-shopping problems
Bibliographic record
Abstract
Background: Compulsive buying-shopping is recognised as a significant mental health concern, yet its aetiology is largely understudied. A known risk factor for compulsive buying-shopping is adverse childhood experiences (ACEs). ACEs are also associated with greater problems regulating emotions, as well as depression and anxiety. These factors are also known to be associated with compulsive buying-shopping problems. In this study, we aimed to test a serial mediation model in which ACEs were associated with compulsive buying-shopping problems via emotion dysregulation, and then emotional psychopathology (depression, anxiety). Methods: We tested this model cross-sectionally in two large samples (N = 1,868 & 4,742) to evaluate the robustness of the model. Both samples completed self-report measures of ACEs, emotional dysregulation, compulsive buying, depression, and anxiety symptoms. Results: We found support for indirect effects, and all results were consistent for both samples. ACEs predicted greater emotion dysregulation, which then predicted greater depression and anxiety. In turn, anxiety (but not depression) predicted compulsive buying symptoms. Discussion and conclusions: Emotion dysregulation and anxiety consistently mediated the relationship between ACEs and compulsive buying symptoms. Both emotion dysregulation and anxiety represent malleable targets in clinical interventions for compulsive buying-shopping problems. Our findings also suggest that anxiety may be a stronger predictor of compulsive buying compared to depression, which may be an important avenue for future researchers to investigate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".