Assessment of Knowledge of Gluten-Free Diet Amongst Food Handlers in Hospitals
Bibliographic record
Abstract
Purpose: When admitted to the hospital, individuals with celiac disease rely on food handlers for provision of safe, uncontaminated gluten-free meals. We aimed to assess the knowledge of gluten-free diet (GFD) amongst individuals involved in meal preparation for patients. Methods: A questionnaire with 10 demographic and 35 test items to assess knowledge of GFD, including workplace scenarios encountered in meal preparation, was administered to food handlers including cooks, utility workers, dietary technicians, and supervisors in 2 tertiary care, university-affiliated hospitals. A score of ≥28 of 35 (≥80%) was considered a “pass”. Results: A total of 72 individuals completed the study, mean age 40.3 ± 1.6 years, 75% female. Only 42 (56.8%) scored ≥80% and achieved a pass. The average score was 75.9% ± 13.4%, range 25.7%–100%. The supervisors had significantly higher scores (87.9% ± 11.4%) than utility workers (73.0% ± 11.4%; P = 0.01) and cooks (71.7% ± 14.5%; P = 0.01). Cooks had the lowest scores with 80% scoring <80%. Females scored higher than males (77.8% vs. 68.8%; P = 0.02). Conclusions: There are significant differences in GFD knowledge amongst various groups involved in food preparation in hospitals. The gaps identified in knowledge can potentially compromise the safety of patients with celiac disease. Targeted interventions to educate hospital food handlers about GFD are warranted. Registered Dietitians can play an important role in providing this education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".