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Record W4414403308 · doi:10.63556/tisej.2025.1566

How Mediterranean Diet Adherence and Eating Disorders Shape Alexithymia Status?

2025· article· en· W4414403308 on OpenAlexaboutno aff
Gökçen Doğan, Elif Koç, Zeynep AHISKALIOĞLU, Nurcan Yabancı Ayhan

Bibliographic record

Venue3 SEKTÖR SOSYAL EKONOMİ DERGİSİ · 2025
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScaleMediterranean dietEating disordersPopulationRisk factor

Abstract

fetched live from OpenAlex

Understanding the relationship of alexithymia with eating disorders is significant because alexithymia is a risk factor for eating disorders. The importance of the Mediterranean diet has been rising because it provides a sustainable eating model. This study aims to examine alexithymia in the light of eating disorders and adherence to a Mediterranean diet. The study was carried out online in March-June 2022 via social networks. The population of the study consists of 501 adults (72,6% females) aged 18-65. The questionnaires administered to the participants included the REZZY Eating Disorder Scale, Eating Attitude Test (EAT-26), Mediterranean Diet Adherence Scale (MEDAS), and Toronto Alexithymia Scale (TAS-20). Participants at risk for an eating disorder were more likely to have scores in the borderline or clinically significant range with respect to alexithymia compared to participants who were not at risk for an eating disorder. The frequency of alexithymia increased as adherence to a Mediterranean diet decreased and MEDAS scores were higher among individuals who were not alexithymic. It is foreseen that adherence to Mediterranean diet can cause positive effects on psychiatric disorders, and people are suggested to adhere to the Mediterranean diet. Moreover, it is thought that alexithymia and eating disorders might be related to each other.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.274
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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