Association between binge eating and physical violence perpetration among U.S. college students
Bibliographic record
Abstract
BACKGROUND: To date, no known research has explored the link between binge eating and physical violence perpetration despite overlapping psychological constructs that underpin these behaviors (i.e., emotion regulation difficulties, impulsivity). Therefore, this study aimed to determine the association between binge eating and self-reported physical violence perpetration. METHODS: Cross-sectional data from four survey years (2016-2020) of the national (U.S.) Healthy Minds Study (N = 6210) were analyzed. Unadjusted (Independent samples t test) and adjusted (logistic regression) analyses were conducted to determine the associations between binge eating in the past four weeks and violence perpetration in the past 12 months, while adjusting for potential confounders. RESULTS: The unadjusted mean number of days of binge eating was significantly higher among participants who reported physical violence perpetration (M = 2.6, SD = 5.2) compared to those who did not (M = 1.8, SD = 3.7). Logistic regression analysis demonstrated that each additional day of binge eating was associated with 5% higher odds (95% confidence interval 1.02-1.09) of self-reported physical violence perpetration, while adjusting for potential confounders. CONCLUSIONS: Results from this study are the first known to identify an association between binge eating and physical violence perpetration among U.S. college students. Findings are supported by the potential mechanistic overlap of emotion regulation and impulsivity associated with both binge eating and violence perpetration, underscoring the need for more research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".