Binge Eating Disorder (BED) in Relation to Addictive Behaviors and Personality Risk Factors
Bibliographic record
Abstract
While there is good evidence that BED is linked to higher-than-expected use of a broad range of addictive behaviors, mechanisms underlying this association are not well understood. Using a mediation-analytical approach with three age- and gender-matched groups - overweight/obese adults with and without BED, and normal-weight control participants – we tested the hypothesis that adults with BED would engage in more addictive behaviors and have higher scores on a personality-risk index than the two control groups. We also anticipated that the relationship between BED and addictive behaviors would be mediated by a high-risk personality profile. The predicted mediation effect was strongly supported. Contrary to expectation BED participants did not engage in more addictive behaviors or have higher personality-risk scores than their weight-matched counterparts. However, both overweight/obese groups did have significantly higher scores than the normal-weight group. The relationships among personality risk, elevated BMI, and addictive behaviors has important clinical and implications, especially for treatments that target psycho-behavioral intervention for compulsive overeating and substance-use disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".