Evaluation and prioritization of food safety risks in the Nigerian red meat industry
Bibliographic record
Abstract
Food safety is a global concern, particularly in developing countries like Nigeria. Hence, this study aims to identify and rank food safety priorities in the red meat industry in Ilorin, Northcentral Nigeria, as a first step towards targeting interventions and resource allocation. A cross-sectional study involved 496 participants in various roles within the red meat industry, including butchers, meat traders, veterinarians, and others. Data were collected through a structured questionnaire administered over eight months in ten slaughterhouses and slaughter slabs in Ilorin. The study assessed knowledge about major concerns on food safety and ranked these concerns based on perceived importance by the participants. The study revealed that 89.5% of 496 participants were aware of food safety, with less than 40.0% having received formal training. However, >85% of participants were aware of contamination risks during carcass processing, and sanitation practices needed more consistency. Participants ranked antemortem and postmortem inspections as the most critical concerns (48.8 and 26.7%, respectively) and meat handling by retailers (0.42%) as the least important concerns. Socio-demographic factors such as age, gender, years of experience, level of education, and role within the industry significantly influenced participants' knowledge and prioritization of food safety issues. The findings indicate a need for a comprehensive training program tailored to the diverse roles within the red meat industry. Improvements in sanitation, transportation, storage, and regular inspections are recommended to enhance food safety standards. These efforts aim to mitigate the risks associated with foodborne diseases while improving red meat products' quality. However, the gap between intent and actual outcomes underscores the need for effective implementation and continuous monitoring of food safety practices.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".