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Record W2602197544 · doi:10.47339/ephj.2014.153

Inspecting inspection reports, does the type of restaurant change the risk?

2014· article· en· W2602197544 on OpenAlexfundvenueaboutno aff
Paul Cseke, Environmental Health BCIT School of Health Sciences, Helen Heacock, Bobby Sidhu, Lorraine McIntyre, Lynn Wilcott

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
FundersBritish Columbia Centre for Disease Control
KeywordsEthnic groupEnvironmental healthFood safetyBusinessHazardMarketingMedicinePolitical science

Abstract

fetched live from OpenAlex


 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.250
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes3
Has abstractyes

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