Exploring the Contributions of Affective Constructs and Interoceptive Awareness to Feeling Fat
Bibliographic record
Abstract
Abstract Purpose Feeling fat, a subjective feeling of being overweight that does not correspond with body weight, is commonly-reported in patients with eating disorders (EDs). Research suggests that feeling fat relates to deficits in interoceptive awareness (IA), the perception and integration of signals related to body states. Relatedly, recent work has linked feeling fat to affective constructs, such as depressive symptoms and guilt. The current study explores the unique relationships between feeling fat, self-reported and objective IA, guilt, alexithymia, and depressive symptoms. Method Female undergraduates (N = 128) completed the 11th item of the Eating Disorder Examination Questionnaire, the Toronto Alexithymia Scale, the Guilt subscale of the Positive and Negative Affect Schedule, and the Beck Depression Inventory-II. Participants also completed two IA measures: a heartbeat perception task and the Multidimensional Assessment of Interoceptive Awareness. Results Results indicated that all collected measures explained 56% of the variability in feeling fat. Depressive symptoms, self-reported IA, and BMI accounted for significant variability in feeling fat. Relative weights analyses revealed that depressive symptoms accounted for the most variability in feeling fat. Conclusions Our results replicate previous findings that depressive symptoms relate significantly to feeling fat and extend this work by suggesting that, for ED patients with interoceptive deficits, depressed affective states contribute to feeling fat. Clarifying this relationship may provide novel targets for ED treatments. For example, endorsement of feeling fat during an intake assessment may alert clinicians to assess for depressive symptoms and focusing on these depressive symptoms in treatment may improve feeling fat. Level of Evidence: Level I: Evidence obtained from an experimental study
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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 | 0.007 |
| 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 | 0.001 | 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".