Improving food safety compliance of potential employees through a novel model of knowledge, attitude, commitment, and practice
Bibliographic record
Abstract
The global burden of foodborne illnesses remains high due to consumption of contaminated food. Vigilant attention from all, particularly food handlers, is needed to address food safety concerns and minimize the risks that affect consumer health, international business, and tourism worldwide. The main objectives of this study were to: (i) evaluate the food safety knowledge, attitudes, commitment (involving affective, continuance, and normative types), and practices of students studying subjects related to food sciences in Jordanian universities and (ii) examine the impact of knowledge and attitude on commitment, and subsequently, on relevant practices using structural equation modeling. The questionnaire was completed by 526 students from 5 universities. The score percentages of students in tested categories were as follows: agreement on food safety knowledge (87.7%), attitude (92%), affective commitment (90.4%), continuance commitment (83%), normative commitment (90.3%), and practices (91.5%). The results showed that associations between food safety knowledge and attitude with the three types of commitment were positive and significant (p < 0.05). Affective and normative commitments acted as partial mediators between food safety knowledge and practices and between food safety attitude and practices, whereas continuance commitment did not act as a mediator. Furthermore, a moderate correlation was found between food safety knowledge and attitude. Focus on food safety commitment drivers is essential, as commitment plays a key role in achieving actual hygienic practices by translating adequate knowledge and positive attitudes to improvements in hygienic behavior and reductions in the risk of foodborne illnesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".