Food Allergy, Eating Disorders and Body Image
Bibliographic record
Abstract
Background/Introduction: Food allergy (FA) management involves avoiding allergenic foods to prevent an adverse reaction. Affected individuals spend substantial amounts of time thinking about food and the impact it has on their bodies. As such, we posit sustained awareness of food choices may contribute to distorted body image and disordered eating. We performed a narrative review to glean insight into associations between FA, body image and eating disorders. Methods: A literature search of Scopus, PsycINFO, PubMed and Google Scholar was conducted connecting terms for "food allergy" with terms for "body image" and "eating disorders". Title and abstract screening were independently performed by two reviewers, with relevant abstracts carried forward to full text screening. Results: 159 articles were eligible for full text screening and a total of 12 publications were ultimately included in this narrative review, and 1 article from the grey literature. Adolescents and adults both reported feeling as though their bodies were "defective" and were found to have integrated their FA as a stable facet of their identities. In addition, FA was overwhelmingly associated with prevalence of eating disorders and eating disorder-like symptoms. Disturbed body image was found to be a mediating variable for the development of disordered patterns of eating in food allergic individuals. Limitations: Few studies appeared to consider comorbidities as confounders. Also, many studies employed convenience sampling, which does limit generalizability of conclusions. Discussion/Conclusion: Based on a small body of literature, there appears to be a potential association between FA and distorted body image and disordered eating.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".