Food Allergy-Related Bullying in Pediatric Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Food allergy (FA)-related bullying is a significant public health concern affecting pediatric patients. This systematic review investigates the prevalence, characteristics, and psychosocial impact of FA-related bullying, as well as current intervention strategies within educational and healthcare settings. METHODS: A systematic literature search was conducted across the PubMed, Web of Science, and CINAHL databases, covering publications up to February 2024. The review followed PRISMA guidelines and included studies on children and adolescents (0-18 years) diagnosed with FAs. Studies were selected based on eligibility criteria and assessed for quality using the Newcastle-Ottawa Scale. RESULTS: The initial search identified a total of 260 records (6 from scientific databases and 254 from registries). Twenty-six studies met the inclusion criteria. The findings of these studies reveal that FA-related bullying is prevalent, with rates varying between 17% and 60%, depending on the study population and methods. Bullying often involves verbal teasing, social exclusion, and physical threats using allergens, presenting both psychological and physical risks. Psychological consequences include increased anxiety, depression, and social withdrawal, which persist over time, significantly impacting quality of life for both children and their families. Notably, bullying often occurs in school settings, emphasizing the need for targeted interventions. CONCLUSION: FA-related bullying profoundly affects mental health and quality of life for affected children and their families. Interventions, such as school-based allergy education programs and policies promoting inclusivity and safety, have shown promise in reducing bullying incidents. A collaborative approach involving healthcare providers, educators, and policymakers is essential to mitigate the impact of FA-related bullying and improve outcomes for affected children.
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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".