Factors influencing workers to follow food safety management systems in meat plants in Ontario, Canada
Bibliographic record
Abstract
Small and medium sized food businesses have been slow to adopt food safety management systems (FSMSs) such as good manufacturing practices and Hazard Analysis Critical Control Point (HACCP). This study identifies factors influencing workers in their implementation of food safety practices in small and medium meat processing establishments in Ontario, Canada. A qualitative approach was used to explore in-plant factors that influence the implementation of FSMSs. Thirteen in-depth interviews in five meat plants and two focus group interviews were conducted. These generated 219 pages of verbatim transcripts which were analysed using NVivo 7 software. Main themes identified in the data related to production systems, organisational characteristics and employee characteristics. A socio-psychological model based on the theory of planned behaviour is proposed to describe how these themes and underlying sub-themes relate to FSMS implementation. Addressing the various factors that influence production workers is expected to enhance FSMS implementation and increase food safety.
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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 | 0.000 |
| 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".