Impact of the COVID-19 pandemic on food safety inspection outcomes in Toronto, Canada: a Bayesian interrupted time series analysis
Bibliographic record
Abstract
Summary The coronavirus disease (COVID-19) pandemic resulted in major disruptions to the food service industry and regulatory food inspections. The objective of this study was to conduct an interrupted time series analysis to investigate the impact of the COVID-19 pandemic on food safety inspection trends in Toronto, Canada. Inspection data for restaurants and take-out establishments were obtained from 2017 to 2022 and ordered as a weekly time series. Bayesian segmented regression was conducted to evaluate the impact of the pandemic on weekly infraction and inspection pass rates. On average, a 0.31-point lower weekly infraction rate (95% credible interval [CI]: 0.23, 0.40) and a 2.0% higher probability of passing inspections (95% CI: 1.1%, 3.0%) were predicted in the pandemic period compared to pre-pandemic. Models predicted lower infraction rates and higher pass rates immediately following the pandemic that were regressing back toward pre-pandemic levels in 2022. Seasonal effects were also identified, with infraction rates highest in April and pass rates lowest in August. The COVID-19 pandemic resulted in an initial positive effect on food safety outcomes in restaurants and take-out food establishments in Toronto, but this effect appears to be temporary. Additional research is needed on seasonal and long-term inspection trends post-pandemic.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".