Eating in response to emotions: Alexithymia, emotional eating, and associated psychological mechanisms
Bibliographic record
Abstract
The overarching objective was to elucidate the relationship between alexithymia and eating in response to emotions. First, a systematic review synthesised the findings of nine eligible articles, providing preliminary evidence for a positive association between alexithymia and self-reported emotional eating. As the Dutch Eating Behaviour Questionnaire (DEBQ-EE) was the subjective emotional eating measure most frequently used by previous research, it became the subject of an exploratory ‘think aloud’ study. This study audio-recorded participants’ spoken aloud thoughts as they completed the DEBQ-EE online. Two cross-sectional studies were conducted to further explore alexithymia and emotional eating using other self-report measures (Emotional Eating Scale [EES] and Salzburg Emotional Eating Scale [SEES]) and identify mechanisms for potential intervention targets. Findings indicated an indirect relationship between alexithymia and emotional eating (EES) via emotion dysregulation, and subsequently a positive conditional indirect effect whereby greater emotion dysregulation and greater self-compassion interacted, leading to greater emotional eating (EES). It was concluded that neither emotion dysregulation nor self-compassion would be appropriate targets for emotional eating interventions. The construct of ‘feeling fat’ was introduced, considered to be a proxy description used when individuals are otherwise unable to identify/describe their negative feelings, and associated with unfavourable outcomes. Existing literature is largely situated within clinical contexts, despite presence within general populations, offering an opportunity to design a brief intervention to test whether encouraging identification and description of feelings would lead to reduced state sensations of feeling fat, within the general population. The findings of the study were unexpected, as despite no significant difference in change scores across groups, the control condition elicited the greatest mean reduction in feeling fat compared to the intervention conditions. A gap in the literature examining the relationship between self-compassion and feeling fat was also examined in this final study, providing preliminary support for an inverse relationship between the traits.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".