Assessing Food Safety Culture: Selecting Methods and Communicating Insights
Bibliographic record
Abstract
The interaction between food safety culture and communication plays a pivotal role in building trust and fostering organizational success.A robust food safety culture promotes good practices and provides a foundation for compelling stories that highlight your accomplishments, ultimately strengthening stakeholder confidence.Choosing the most suitable method for assessing your food safety culture can be challenging.In this article, we offer seven questions to consider when selecting a method, and discuss how to choose one that aligns with your organization's maturity level in food safety culture.Effective communication is a vital aspect of a strong food safety culture, enabling you to share your food safety successes with employees, customers, and regulators.By integrating communication strategies into your food safety practices, you can improve transparency, raise awareness, and contribute to a safer and more reliable food environment.The authors' organization provides various tools to assess and enhance food safety culture while sharing best practices for communication among food safety professionals.The insights in this article are based on validated best practices and a roundtable discussion at the Food Safety Summit in May 2024, featuring experts like Mark Beaumont, Vice President of Quality and Food Safety Standards and Risk Management at Danone; Andrew Clarke, Senior Director of Quality Assurance at Loblaw Companies Ltd.; Janet Riley, Owner and Assessing Food Safety Culture: Selecting Methods and Communicating Insights As food safety professionals, recognizing the importance of both food safety culture and effective communication is crucial
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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 | 0.162 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".