The roles of negative affect and emotion differentiation in the experience of ‘feeling fat’ among undergraduate students: An ecological momentary assessment study
Bibliographic record
Abstract
'Feeling fat' is the somatic experience of being overweight not fully explained by objective body mass. According to the body displacement hypothesis, 'feeling fat' occurs when diffuse negative emotions are projected onto the body in lieu of adaptive emotion regulation. Emotion differentiation, the ability to experience and label discrete emotions, is an important skill for adaptively addressing emotion that may reduce 'feeling fat.' We hypothesized that individuals with better negative emotion differentiation would be less likely to report 'feeling fat' when experiencing high negative emotion. We collected ecological momentary assessment data from 198 undergraduate students (52.24% female). Multilevel modeling revealed that both within-person increases in negative emotions and the tendency to experience greater negative emotion were associated with greater 'feeling fat.' Of the specific types of negative emotion, guilt and sadness predicted 'feeling fat.' Contrary to hypotheses, individuals with better emotion differentiation were more likely to report 'feeling fat' after experiencing elevated negative affect. These findings contradict the primary clinical conceptualization of 'feeling fat,' suggesting that factors beyond displacement of negative emotions onto the body may be responsible for 'feeling fat'. Results in a sample with pronounced shape/weight concern may better support the traditional clinical understanding of 'feeling fat.'
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".