Phenomenological support for escape theory: a qualitative study using explicitation interviews with emotional eaters
Bibliographic record
Abstract
The current study explored the phenomenology of emotional eating, that is, the descriptive knowledge of what one perceives, senses, and knows in one's immediate awareness and experience during emotional eating. Eight individuals with emotional eating were interviewed twice using explicitation interviewing. Data were analyzed using thematic analysis, which resulted in nine themes describing the diachronic (or temporal) unfolding of emotional eating and several sub-themes that described various synchronic (or experiential) dimensions of this unfolding. The core findings of this study support the escape theory of emotional eating and recommend future directions to investigate the self-related shifts proposed by this theory. Namely, the findings show that individuals tend to use food to regulate their emotions by reducing the unpleasant experience of negative emotions and the associated unpleasant narrative processing or ruminations about stressors that caused the negative emotions. This then leads to an urge to eat associated with a desire for the sensory experience of eating. Eating then enables individuals to reduce thoughts about their stressors and bring themselves into the present moment through embodiment. Future quantitative research could investigate this mechanism of shifting from narrative to embodied processing to regulate emotions in emotional eating to develop treatment programs, such as mindfulness-based programs, that could encourage such a shift and emotion regulation without the use of food.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".