Emotional eating and weight regulation: a qualitative study of compensatory behaviors and concerns
Bibliographic record
Abstract
BACKGROUND: Emotional eating, or overeating in response to negative emotions, is a behavior endorsed by both normal weight and people with overweight/obesity. For some individuals, emotional eating contributes to weight gain and difficulties losing weight. However, there are also many who engage in emotional eating who maintain a normal weight. Little is known about the mechanisms by which these individuals are able to regulate their weight. METHODS: The present study seeks to gain insight into the behaviors of individuals of normal weight who engage in emotional eating through a series of one-on-one, 1-h long, qualitative interviews. Interviews were semi-structured and guided by questions pertaining to participants' compensatory behaviors used to regulate weight and concerns regarding their emotional eating. All interviews were transcribed and then objected to a thematic analysis of their content. RESULTS: The results of this analysis showed that participants endorsed using physical activity, controlling their eating behaviors, and engaging in alternative stress reduction and coping strategies to mitigate the effects of their emotional eating. They reported concern over the effects of emotional eating on their weight, body image, and health and saw this behavior as an unhealthy coping mechanism that was difficult to control. CONCLUSIONS: These results suggest that programs promoting exercise, mindful eating, emotion regulation, and positive body image could have a positive effect on emotional eaters who struggle to maintain a healthy weight.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".