Alexithymia and binge eating in obese outpatients who are starting a weight‐loss program: A structural equation analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate whether obese patients with binge eating (BE) have higher alexithymic features; to explore the different relationships between psychological features (alexithymia, depression, and anxiety) and BE. METHOD: Three hundred sixty one obese BE-patients were evaluated for alexithymia, psychological distress, and BE. Alexithymia was measured with the 20-item Toronto Alexithymia Scale (TAS-20); BE was assessed with the BE Scale (BES), and depression and anxiety symptoms were evaluated with the Hospital Anxiety and Depression Scale (HADS). RESULTS: Patients with BE reported significantly higher TAS-20 total scores than those without BE (p < .001). The SEM analysis showed that the difficulty in identifying feelings (DIF) and difficulty in describing feelings (DDF) components of alexithymia affected BE along different pathways. DIF was found as a major factor influencing altered eating both directly (p = .20*) and above all through the mediation of psychological distress (p = .19***), whereas DDF affected BE only through psychological distress at a lesser extent (p = .09**). DISCUSSION: Alexithymic difficulties in affective awareness may play an important role in the onset and maintenance of BE, especially when patients experienced anxiety and depression symptoms. Clinicians involved in the management of obesity should address the combination of alexithymic traits and emotional distress by planning effective client-focused interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".