A systematic review of the knowledge and training of food service workers on food allergies
Bibliographic record
Abstract
Background Food allergies are adverse reactions to a specific food antigen which is mediated by immunological mechanisms and are fast rising to become a significant public health concern. Around 1% of the world's adult population suffers from food allergies. The prevalence of food allergies which can be life-threatening is commonly estimated to affect 3–5% of the adult population in North America. Objectives The purpose of this study is to review published food allergy knowledge and training amongst food service workers and identify the policies in place concerning food allergies globally. Methodology Documented food service workers' knowledge and training about food allergies published between September 2006 and February 2021 were comprehensively reviewed. A widespread literature search was carried out using subject headings, search terms, and keywords. Results were examined in groups to explore patterns through research. Results A total of 18 relevant studies that analyzed the food allergies knowledge and training of food service workers were reviewed. Eight studies (44%) were performed in the USA, followed by two studies (11%) in the UK, and one study each (5%) for New Zealand, Turkey, Malaysia, France, Western Romania, Germany, Brazil, and Canada. In the studies, respondents were asked a series of questions to assess their level of knowledge and the types of training relating to food allergies they received. Conclusions This study identified the gaps in policy, as well as knowledge and training among food service workers, to manage food allergies safely, thus emphasizing the importance and need for food allergy training.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".