Inspecting inspection reports, does the type of restaurant change the risk?
Bibliographic record
Abstract
Background An estimated of 4 million Canadians (one in eight people) become ill every year from a food-borne illness (Thomas et al., 2013). The economic and social burdens of these illnesses are vast. As restaurants are a big sector of the food industry, improving their food safety would reduce the risk of food-borne illnesses. Environmental Health Officers (EHOs) are on the front line, educating restaurant operators in order to improve food safety. In Metro Vancouver there are many different types of ethnicities and types of restaurants; this provides a challenge for EHOs to know where to allocate their time and resources. Methods The author analyzed 150 Fraser Health inspection reports in the Burnaby, New Westminster and Surrey municipalities. The restaurants fell into three different categories: i) Independently owned ethnic, ii) Independently owned, non-ethnic and iii) chain non-ethnic restaurants. Hazard ratings, number of critical and number of non-critical violations from their latest inspection report were compared. Each violation code was also recorded to identify any infraction trends that exist. Results Analysis of the number of critical violations identified ethnic, chain non-ethnic, and independent non-ethnic restaurants as not being significantly different (p=0.09). The number of non-critical violations was different (0.033), with ethnic restaurants having the most. The number of critical violations, when treating each ethnicity as its own category, is however significantly different (p=0.044) between restaurant types. There was a significant association between hazard rating and restaurant type, with independent ethnic restaurants having the worst hazard rating (p=0.017). Conclusion The type of ownership (independent vs chain) and the restaurant type were not a factor when looking at number of critical violations that a restaurant commits. Independent ethnic restaurants had a slightly higher mean number of critical violations. Japanese restaurants had the highest number of critical violations out of the three ethnicities studied. These findings suggest a slight disparity in risk to public health between ethnic and non-ethnic restaurants.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".