Public knowledge of food poisoning, risk perception and food safety practices in Saudi Arabia: A cross-sectional survey following foodborne botulism outbreak
Bibliographic record
Abstract
To investigate food poisoning knowledge, risk perception and safe food handling practices among Saudi Arabian public following foodborne botulism outbreak. A cross-sectional survey targeting the Saudi Arabian public between May 6 to 20, 2024, following the first foodborne botulism outbreak. Infectious disease and public health experts developed survey questions according to Saudi Public Health Authority and Ministry of Health (MOH) guidelines, and distributed surveys through social media. Of 3779 participants, 73.1% were female and 50.1% were aged 18 to 24 years. Almost one-third (30.2%) reported a previous food poisoning experience, with an incidence of 71.7 cases per 1000 person years. The most common perceived source of FP was restaurants foods (80.3%). The overall knowledge score of the participants regarding food poisoning was 3.42 ± 1.57 out of 7. The mean food safety practice score was 3.70 ± 1.42 out of 9. Multivariable regression analysis showed individuals aged 35 years or older (β = 0.205, P < .001), those who were married (β = 0.204, P = .003), participants with previous (FP) experience (β = 0.089, P = .009), and those who relied on information from the Ministry of Health or medical publications regarding FP (P < .001) exhibited significantly higher practice scores than other groups. The least adherence to safe practices were noted among the following: routine use of thermometer during cooking (2.7%), avoidance of washing raw chicken (13.7%) and washing hands after using cellphone during cooking (26.1%). The FP knowledge score did not correlate significantly with practice score (P = .065). This study highlights the significant knowledge gaps and inadequate food safety practices among the public in Saudi Arabia. Although certain groups, including adults (>35 years), married individuals, and those with previous food poisoning experience, showed greater adherence to safe food handling practices, adherence to specific preventive measures remained generally low. These findings highlight the need for targeted educational initiatives and interventions to improve food safety awareness and practices across diverse demographic groups in Saudi Arabia. The integration of generative AI tools, such as ChatGPT, as a public resource for food poisoning information, presents a new opportunity, but it requires further research and development to ensure accuracy and reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".