Maladaptive Eating Behaviors and Childhood Trauma: A Focus on Food Addiction
Bibliographic record
Abstract
Background Many studies have highlighted the clinical relevance of food addiction (FA) by showing its association with psychopathological severity and even more when co-occurring with binge eating disorder. It was suggested that the association between FA and greater psychopathological severity could be accounted for by a history of traumatic experiences. The present study examined the relationship between childhood trauma (including peer victimization, abuse, and neglect) and maladaptive eating behaviors (FA, binge eating, and grazing) and explored whether childhood trauma predicts FA when controlling for binge eating, grazing, and other confounding variables. Methods One hundred fourteen adult women seeking psychological help for problems related to eating or weight completed questionnaires measuring FA, binge eating, grazing, depressive symptoms, peer victimization, and childhood abuse and neglect. Results FA showed significantly small to moderate positive correlations with all measures of childhood trauma, except for physical neglect. A hierarchical regression, including binge eating, grazing, depressive symptoms, age, and childhood trauma explained 55% of FA variance, with 7% of variance explained uniquely by childhood trauma. Conclusions This study supports that FA is related to childhood trauma and provides a potential explanation for the association of FA with greater psychopathological severity. From a clinical perspective, FA provides an accurate and quick assessment of psychopathological severity and represents an essential complement to the evaluation of eating disorders related to overweight. Future studies should attempt to estimate the impact of childhood trauma on treatment outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".