Who Are More Competent in Food Safety: Veterinary Medicine or Food Hygiene Students?
Bibliographic record
Abstract
In response to the decreasing number of veterinary medicine graduates interested in working in the food sector and public health, the competency of students from a food safety–related university major—named “Food Hygiene” (FH) for taking the responsibilities of a veterinarian in the field of hygienic food production—was assessed in the present study. In this regard, a cross-sectional study was conducted in 2022 at Ferdowsi University of Mashhad, Iran, to evaluate the food safety knowledge (K), attitude (A), and practice (P) among the students from FH ( n = 73), veterinary medicine (vet, n = 28), and other majors ( n = 40). Results showed that FH and vet respondents demonstrated comparable food safety knowledge (∼58%), attitude (68.8%–74-8%), and practice (71.3%–76.4%) scores. Moreover, based on the detailed responses, the FH participants could satisfactorily respond to the questions regarding the safe production, preservation, and handling of foods of animal origin. In conclusion, our findings reveal the potential capability of an FH graduate to handle the tasks of public health veterinarians, particularly in the field of primary production hygiene of animal-based foods. Moreover, based on our findings, it is recommended that additional specific courses related to the production, processing, and hygiene of nonanimal food products be added to the FH program.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".