Enhancing Food Safety and Infection Control in Mass Foodservice Operations: Implementing a Foodservice Provision Audit Tool for Sport (FPAT‐S)
Bibliographic record
Abstract
Ensuring food safety and infection control in large-scale foodservice operations is critical to protecting health in mass feeding environments, including athletes' dining halls and institutional dining settings. Athletes at major competitions are at risk of illness in overcrowded dining areas, especially where foodservice safety standards are inconsistent. OBJECTIVES: This study aimed to (1) develop and pilot-test a Foodservice Provision Audit Tool for Sport (FPAT-S) at two major sport competitions to evaluate compliance with infection control and food safety measures, and (2) determine its inter-rater reliability. METHODS: The FPAT-S was tested by health professionals during the 2022 Canada Summer (n = 12) and 2023 Winter Games (n = 9). The tool included 19 questions with binary, multiple-choice, and Likert scale responses. Compliance trends over time and inter-rater reliability were analyzed. RESULTS: Hand sanitizer availability exceeded 75% compliance and improved over time, whereas staff sanitation compliance declined by the end of both events. Physical distancing compliance remained below 50% throughout. Binary response questions showed moderate agreement (κ = 0.471, p = 0.028) between auditors compared to scale and multiple-choice questions during the Summer phase, while agreement was lower and non-significant for scale and multiple-choice responses in both phases. Auditor variability was attributed to subjectivity and audit timing. CONCLUSION: The FPAT-S provides a structured approach to assessing food safety and infection control in mass foodservice settings. With refinement, it can support dietitians and foodservice managers in maintaining compliance across a range of institutional and commercial operations, beyond sporting events, and inform future public health infection control strategies.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".