Food safety knowledge, attitudes, practices, and associated factors: A cross-sectional survey of Nepalese consumers
Bibliographic record
Abstract
Foodborne illnesses remain a significant public health concern in low- and middle-income countries like Nepal. This study assessed the knowledge, attitudes, and practices (KAP) of Nepalese consumers regarding food safety and identified associated sociodemographic factors. A cross-sectional online survey was conducted among 620 participants using a structured questionnaire comprising 60 KAP-related questions. Logistic regression analyses were used to explore associations between KAP scores and participants' characteristics. The median scores for each knowledge, attitudes, and practices domain were 18/20 (90 %), 16/20 (80 %), and 13/20 (65 %), respectively. In the multivariable logistic regression analysis, education level was significantly associated with knowledge and attitudes domains: knowledge ( p < 0.001) and attitudes ( p < 0.001), with participants holding higher education generally more likely to report appropriate food safety knowledge and attitudes. However, practices related to food safety were not associated with the level of education. Gender was significantly associated with food safety practices ( p = 0.012), and male participants had 36 % lower odds of having a good practices level compared to female participants (OR=0.64; 95 % CI: 0.45–0.91). Overall, despite demonstrating better knowledge and attitudes, participants in our study exhibited a comparatively lower levels of practices. This finding indicates a substantial gap between KAP domains, highlighting the need for targeted public health interventions, behavior-focused education, and gender-sensitive strategies to improve food safety practices and reduce the burden of foodborne diseases in Nepal.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".