Factors influencing food safety and good manufacturing practices in ready-to-eat meat processing plants in Ontario, Canada: A qualitative study
Bibliographic record
Abstract
Inspections of ready-to-eat (RTE) meat processing plants in Canada have identified a lack of compliance with good manufacturing practices (GMPs), indicating a need to improve food safety practices to protect consumers. The objective was to conduct a qualitative study to explore factors influencing food safety practices and GMPs in meat processing plants. Semi-structured interviews and open-ended online surveys were conducted among RTE meat processing plant operators in Ontario, Canada. Across all 18 participants, most were owners (n = 9, 50%) and indicated they had worked in their plant for more than 10 years (n = 10, 59%). Three themes were generated through reflexive thematic analysis: (i) education, experience, strong core values, and positive attitudes contribute to safe and high-quality food production, (ii) day-to-day challenges are exacerbated by inadequate resources, changing regulations, and a fractured relationship with government, and (iii) on-the-ground changes are needed to improve plant productivity and reduce external hurdles. Operators from this study shared opportunities for improvement to tackle existing food safety challenges faced by the meat industry. These included facilitating dialogue with the government, providing program planning support, and increased professional development opportunities for meat operators. These findings can help shape government priorities as they pertain to meat processing plants, and support interventions aimed at improving food safety and GMPs in the RTE meat and poultry sector. • Ongoing challenges related to equipment maintenance, personnel, and changing regulations exist. • Operators wanted greater program planning support, professional development opportunities, and dialogue with government. • Stakeholder dialogues, review of provincial inspection frameworks, and prioritizing a food safety culture are recommended.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".