Food allergy knowledge, attitudes and practices (KAP) among restaurant's staff in Jordan: a cross-sectional study
Bibliographic record
Abstract
Purpose People with food allergies rely to a great extent on restaurant staff to have a safe meal. The purpose of this paper is to acquire novel data to evaluate knowledge, attitudes and practices of food allergy among the restaurant's staff and factors that could be associated with these domains. Design/methodology/approach A cross-sectional study involving 306 restaurant staff in Amman City, Jordan was conducted using a validated self-reported questionnaire. Logistic regression analysis was performed to identify factors independently associated with knowledge, attitude and practices (KAP). Findings The results showed that most restaurant staff had moderate to excellent knowledge, positive attitudes, low to moderate risk practices regarding food allergy. Knowledge level was significantly associated with the staff's age, position, education level and restaurant class. Further, attitude level was significantly associated with staff age, experience, gender, education level, restaurant class and whether the restaurant held food safety certificates. Similarly, staff behavioral practices were significantly associated with staff position, restaurant class and the presence of valid food safety certificates. Practical implications This study suggests that more training for the staff in restaurants would be associated with a lower risk of providing unsafe meals. Originality/value This study demonstrated the need for the pre-employment theoretical and practical tests for restaurants’ staff to determine their knowledge level and provide them mandatory developing training programs to support and facilitate food allergy risk management and to develop food allergy policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".