Dairy industry employee knowledge, attitudes and practices in response to COVID -19 policies in Jordan
Bibliographic record
Abstract
Purpose This study examined the level of knowledge, attitudes and practices (KAP) of Jordanian dairy employees about coronavirus disease 2019 (COVID-19) characteristics and the effect of precautionary measures on food safety risk during the pandemic. Design/methodology/approach A cross-sectional study was conducted between Dec 17, 2020 and Feb 22, 2021, involving a total of 428 participants across 34 random chosen dairy facilities in Jordan. KAP related to COVID-19 were measured by 46 items, while 13 items were used to examine perceived notions regarding COVID-19 precautionary measures on food safety. Findings The results indicated that 32.2% of the respondents had sufficient knowledge, 60.3% had a good attitude, and 27.1% followed correct practices concerning COVID-19. Moreover, female respondents had higher total KAP scores of COVID-19 characteristics than males. Furthermore, older and more experienced respondents had higher total KAP scores than younger respondents. This study also observed that the total KAP scores were not affected by education, marital status, and job position. Characteristics and measures taken by the dairy industry were at large significantly associated with (p < 0.05) knowledge and practice of employees about COVID-19 attributes. Results of this study suggested that Jordanian dairy workers were not adequately aware about COVID-19. Originality/value No such study on dairy workers has been conducted previously to the best of the authors’ knowledge. Moreover, studies which analyse the association of industry response and characteristics on the KAP of employees are very limited.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".