Consumer knowledge and practice pertaining to safe food-handling in households
Bibliographic record
Abstract
Foodborne illness is the largest class of emerging infectious diseases in Canada. Health Canada estimates that every year in Canada there are 2.2 million cases of foodborne illnesses and 31 of these people die. Most of these cases are preventable. Health Canada estimates that the annual costs related to these illnesses and deaths exceed $2 billion due to medical and hospital expenses, lost income, time off work and loss of business to the food processor or retailer. In 2- 3% of cases, foodborne illnesses can lead to chronic health problems. Illnesses such as chronic arthritis and hemolytic uremic syndrome (HUS), leading to kidney failure, have long-term consequences for the individuals affected and for society and the economy as a whole. This study was conducted to, a) determine the knowledge residents of the Greater Vancouver Regional District (GVRD) have regarding preventative measures and causes of foodborne illness and, b) establish the practices of the target population in two important areas in the prevention of foodborne illness in the home: cross-contamination between raw meat and other foods, and cooking foods sufficiently to eliminate pathogens. A self-administered mail questionnaire was the contact method of choice. Municipalities of Greater Vancouver were stratified into three strata to ensure that the diverse population of the GVRD would be represented in the target population with regards to socio-ethnic variables. The survey was conducted between May 19th and June 30th 1998. Of the 1,600 households contacted, 582 returned a valid questionnaire. A large segment of the persons who are primarily responsible for household meal preparation in the Greater Vancouver Regional District, specifically young adults under the age of 35 and males, reported using inappropriate hygiene practices during meal preparation to prevent foodborne illness. This segment of the population reported using incorrect methods to determine whether meat was cooked sufficiently to prevent foodborne illness and preparation practices that lead to cross-contamination between kitchen utensils and ready-to-eat foods. Furthermore, this population indicated they had incorrect knowledge of the causes and symptoms of foodborne illness. Use of simple techniques before, during and after food preparation will significantly reduce the risk of foodborne illness. Education of the general public, and in particular young adults and females, is important and suggested educational approaches include food labelling, the media, health professionals, primary school curriculum, and offering of training courses in the community. However, further research is needed to determine the most effective way to educate the public to improve food safety knowledge and practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".