Critical food safety violations in Surrey
Bibliographic record
Abstract
Background: Foodborne illness affects 4 million (1 in 8) Canadians each year, with at least 50% of these illnesses linked to restaurants. Environmental Health Officers (EHOs) conduct routine, demand, and follow-up restaurant inspections to safeguard the public. Critical violations (CVs) must be corrected during inspection because they have a high probability of causing a foodborne illness. Examples of CVs include: previously served food not being discarded, and infrequent handwashing from employees. Previous research has shown that individuals of low socioeconomic status are more susceptible to foodborne illness. According to Statistics Canada, the poverty rate in Surrey, British Columbia, is 14.8%, which is slightly higher than the national rate of 14.2%. Unfortunately, there is limited research that assesses the safety of food service establishments in different socioeconomic neighbourhoods. This study examined the relationship between the number of CVs in chain and independent restaurants and median household income in three communities within Surrey. Methods: Secondary data was used for this study. The researcher collected publicly accessible restaurant inspection reports from the Fraser Health website. Three communities (Whalley, Fleetwood, South Surrey) within Surrey were selected for comparison according to their median household income (from City of Surrey Community Demographic Profiles webpage). Whalley and South Surrey had the lowest and highest median household income, respectively. Fleetwood was chosen based on its proximity to the median household income for Surrey. The researcher then recorded the name and restaurant type within these communities using Zomato. 25 chain and 25 independent restaurants were randomly selected in each community. In total, 150 restaurants were analyzed. The number of CVs, violation code, and hazard rating were compared between January 2016 and December 2017. Results: Independent restaurants were found to have more CVs than chain restaurants in all communities. There was an association between the number of CVs observed in both types of restaurants and the restaurant's hazard rating. The p-values for chain restaurants in Whalley, Fleetwood, and South Surrey are: 0.00, 0.00006, and 0.00, respectively. Meanwhile the p-values for independent restaurants in all three communities are 0.00. In general, independent restaurants had more moderate or high hazard ratings than chain restaurants. The top four CVs found in all communities were related to poor sanitation of equipment, improper storage of cold potentially hazardous foods,and lack of adequate handwashing stations. Finally, a negative correlation was observed between the number of CVs in both restaurant types and the neighbourhood median household income (p-value for chain and independent restaurants = 0.0186 and 0.0073, respectively). Conclusion: The findings indicate that communities with lower median household income had more CVs. Further research is needed to analyze this relationship. In addition, chain restaurants have fewer CVs than independent restaurants possibly due to their internal food safety monitoring systems. Therefore, independent restaurants may benefit from more education because this pattern has been observed in the past. Finally, an educational intervention is potentially necessary for restaurant operators in Surrey to reduce the top four CVs, thereby improving the restaurants' hazard rating.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".