Dinesafe Toronto
Bibliographic record
Abstract

 Background: The purpose of this research study was to analyse the success of Toronto’s placard system (Dinesafe) in reducing the number of violations in food service establishments. The placard system is designed to inform the public about restaurant inspection results and to boost operator compliance. Inspections are a point-in-time check of the facility’s ability to manage the risk it poses to public health. It is accepted that if best practices are implemented as designed by an establishment’s food safety and sanitation plan, the risk of a foodborne illness/outbreak can be minimized. Methods: From the Dinesafe program, the number of violations cited at each inspection from all relevant food service establishments receiving a conditional pass from two time periods, 2004-2006 (Before) and 2012-2014 (After), were compared to see if there was a decrease in violations. The reports, completed by Public Health Inspectors (PHI), were retrieved from a publicly available website. Data were analysed using a two-sample T-test. Results: The anticipated decrease in violations in the second time frame was not significant [p = 0.85] nor strong (α = 0.001). The means were similar (3.83 Before and 3.71 After), with standard deviations of 1.91 and 1.79 respectively. A greater number of restaurants were cited in the After analysis (3169 compared to 572). Inspections from 2004-2006 had fewer violations (12 or less) than 2012-1014 (14 or less). The majority of violations (71% Before and 73% After) were between 2 and 4. Reoffenders comprised of 16.3% of total violations in 2004-2006 and 17.5% in 2012-2014. Conclusion: There is no evidence that the placard system has decreased violations or that counting the number of violations a good measure for compliance. Pushback among operators could explain the increase in the number of establishments cited. The increase in maximum citation could be due to an increase in citations available from 2012-2014. The number of establishments that received a conditional pass twice in a time frame increased from 59% to 68%. The maximum number of times an establishment received a conditional pass dropped from 10 to 8. It is recommended that Health Units use plain language narrative on the website rather than violations as a measure to communicate findings to the public. The placard significance should be better communicated to the public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".