Critical Food Safety Violations in Food Facilities by Regions Across Calgary Communities From 2022 Versus 2023
Bibliographic record
Abstract
Critical food safety violations in food facilities are severe infractions that may affect the quality and safety of foods as well as the health and wellbeing of consumers in Canada. In 2023, Alberta Health Services heightened scrutiny on these violations in Calgary. However, no investigation has been undertaken to document its potential effects on types and trend (from the previous year) of food safety violations in food facilities across Calgary regions. The purpose of this quantitative study was to examine the associations between Calgary regions of Canada, Year (2022 versus 2023), and the number of types of critical food safety violations (temperature control deviations, poor sanitation, pest control issues, poor personal hygiene, and unsuitable food conditions). Year and region were the predictor variables and critical food safety violations were the outcome variable. The study was based on the Epidemiology Triangle Theory, which explains that infections are caused by the interactions of the environment, the agent, and the host. Secondary data from inspection reports by Calgary zone of Alberta Health Services between 2022 and 2023 (N = 877) were used. Based on chi-square test of independence, there was a statistically significant association between violation type and year [χ²(4, N = 877) = 11.94, p = .018] as well as between violation type and region [χ²(12, N = 877) = 35.46, p < .001]. In 2023, there were reduced number of poor sanitation and temperature control deviations compared to 2022, suggesting that recent efforts may have some benefits on food safety in Calgary. Findings, though preliminary, highlight the need for more public health interventions to reduce food safety violations in this region and identify the areas most impacted on which such interventions should be targeted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".