Food safety knowledge, attitude and practices among management and science university students, Shah Alam
Bibliographic record
Abstract
In industrialized countries, about 30% of the population may suffer from foodborne illnesses each year. Malaysia has proven to be a country that has very serious focus on food, but the people do not care much about their food safety. Food safety and hygiene is usually explained as the multiple conditions and ways to preserve food quality, so that foodborne diseases and contamination is prevented. University students face with high risk because of their unsafe behaviors in consuming food. There have been many studies accomplished on comprehension, behavior and implementation towards food safety among food handlers. The current research desires to determine the current knowledge, attitude and practice status among students in Management & Science University (MSU) on food safety. The study framework focuses on testing the relationship between knowledge, attitude and practices and whether or not attitude will be a mediating factor towards practices on food safety among university students in MSU. Using a quantitative and descriptive method, a structured questionnaire was distributed among 430 respondents. The result of the study was analyzed using regression test and indirect method. The result shows that the students had a good knowledge, attitude and practice status towards food safety and attitude was a partial mediating factor in the relationship between knowledge and practice.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".