The Clinical Utility of Food Addiction: Characteristics and Psychosocial Impairments in a Treatment-Seeking Sample
Bibliographic record
Abstract
Little is known about the characteristics of individuals seeking treatment for food addiction (FA), and the clinical utility of FA has yet to be established. To address these gaps, we examined (i) the demographic, eating pathology, and psychiatric conditions associated with FA and (ii) whether FA is associated with psychosocial impairments when accounting for eating-related and other psychopathology. Forty-six patients seeking treatment for FA completed self-report questionnaires and semi-structured clinical interviews. The majority of the sample were women and self-identified as White, with a mean age of 43 years. Most participants (83.3%) presented with a comorbid psychiatric condition, most commonly anxiety and mood disorders, with a mean of 2.31 comorbid conditions. FA was associated with binge eating severity and anxiety symptoms, as well as psychological, physical, and social impairment. In regression analyses controlling for binge eating severity, food cravings, depression, and anxiety, FA remained a significant predictor only of social impairment. Taken together, the results suggest that individuals seeking treatment for FA are likely to present with significant comorbid conditions, in particular anxiety disorders. The results of the present research provide evidence for the clinical utility of FA, particularly in explaining social impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".