Understanding the associations between social and emotional expression, communication, and relationships in individuals with eating pathology
Bibliographic record
Abstract
Abstract Research suggests that a disproportionate number of female individuals being treated for an eating disorder (ED) also have autism spectrum disorder (ASD). Alexithymia, or difficulty identifying and describing emotions, may mediate the relationship between ED and ASD. In this study, we explored the association of autistic traits with symptoms of alexithymia and eating pathology, as well as the potential mediating role of alexithymia. Two hundred and twenty‐eight female participants aged 18 and older were recruited from online ED support platforms to complete an anonymous online survey via Qualtrics. The survey included three questionnaires: the Toronto Alexithymia Scale‐20, the Autism‐Spectrum Quotient (AQ), and the 13‐item Eating Disorder Examination Questionnaire. More than half (54.8%) of participants met the clinical threshold on the AQ. Participants with a positive screen on the AQ scale also reported more symptoms of alexithymia (92.6% of individuals with a positive AQ vs. 79.8% of those without), B = 9.02, p < 0.001. A positive AQ screen was also associated with significantly greater disordered eating symptoms, B = 4.26, p = 0.031. Alexithymia mediated this association, a × b = 1.98, p < 0.05. The results establish a strong positive relationship between autistic traits and alexithymia, supporting previous data and suggesting that autistic female individuals struggle to identify emotions. Additionally, alexithymia served as a mediator between autistic traits and disordered eating. Understanding this relationship may help inform the treatment of autistic female individuals who are also struggling with ED.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".