Trauma, Alexithymia And Binge Eating
Bibliographic record
Abstract
Introduction: Binge eating disorder was recently recognized as a clinical entity in the diagnostic manuals. More studies are needed exploring the different correlates of binge eating symptoms. Objectives: explore associations between and the predictive role of having experienced traumatic experiences and of alexithymia and/to binge eating symptoms.Methods: 421 participants from the general population and college students (female, n = 300; 71,3%; mean age = 30,7, SD = 11,51) responded to the Traumatic Events Checklist, the Binge Eating Scale and the Toronto Alexithymia Scale-20.Results: Binge eating prevalence was similar to values found in other Portuguese studies. There was a significant association between body mass index (BMI) and binge eating total score, in both genders. Only in the female subsample, reported sexual trauma, family trauma and total of traumatic experiences showed associations with the binge eating total score. In both genders, the binge eating total score correlated with difficulties in identifying and describing feelings (TAS-20). In the female subsample women the binge eating total score also correlated with the external thinking style (TAS-20). Women that reported being overweight or having obesity in childhood presented current higher binge eating levels. The BMI, the total of traumatic experiences and the difficulty in identifying feelings significantly predicted the binge eating total score.Conclusions: This study confirms the role of BMI as a correlate of binge eating and shows that the number of traumatic experiences and the presence of higher levels of alexithymia are associated with binge eating symptoms.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".