Ready-to-eat Meat Plant Characteristics Associated with Food Safety Deficiencies During Regulatory Compliance Audits, Ontario, Canada
Bibliographic record
Abstract
Food safety deficiencies in ready-to-eat (RTE) meat processing plants can increase foodborne disease risks. The purpose of this study was to identify common deficiencies and factors related to improved food safety performance in RTE meat plants in Ontario. Routine food safety audit records for licensed provincial free-standing meat processing plants (FSMPs) and abattoirs that process RTE meats were obtained and analyzed in Ontario, Canada, from 2015 to 2019. A Bayesian regression analysis was conducted to examine the association between selected plant characteristics and two outcomes: overall audit rating (pass vs. conditional pass or fail) and individual audit item fail rate. The audit rating was examined in a logistic model, while the audit item fail rate was evaluated in a negative binomial model. The majority (87.7%, n = 800/912) of audits resulted in a pass rating (compared to conditional pass or fail). The mean number of employees per plant, among 200/204 plants with employee data available, was 11.6 (SD = 20.6, range = 1-200). For the logistic regression model, FSMPs were predicted to have a much higher probability of passing audits than abattoirs (32.0% on average, with a 95% credible interval [CI] of 13.8-52.8%). The number of plant employees, water source (municipal vs. private), and types of RTE meat products produced had little to no consistent association with this outcome. The negative binomial model predicted a -0.009 points lower fail rate, on average, for audit items among FSMPs than abattoirs (95% CI: -0.001, -0.018). Meat plants producing jerky had a higher audit item fail rate compared to those that did not produce such products. The other investigated variables had little to no association with this outcome. The results found in this study can support and guide future inspection, audit and outreach efforts to reduce foodborne illness risks associated with RTE meats.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".