Determinants of Food Thermometer Use and Poultry Washing among Canadian Consumers
Bibliographic record
Abstract
ABSTRACT: Previous surveys have found that few Canadians report using a food thermometer to check cooking doneness, and many report rinsing or washing poultry prior to cooking. A cross-sectional survey study was conducted to investigate the sociodemographic and psychosocial determinants of the reported use of these behaviors among Canadians. A questionnaire was developed, guided by the Theoretical Domains Framework, and pretested through 10 cognitive interviews. The questionnaire was administered in English and French on 18 November 2019, to an online panel of 524 Canadian consumers. Logistic and ordinal regression models were constructed to evaluate determinants of consumers' reported thermometer ownership (yes or no) and thermometer use and poultry washing frequencies (each measured on a 5-point Likert scale). Nearly two-thirds of respondents (64%; n = 333) reported owning a food thermometer. Thermometer ownership was more common among males (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.02, 2.15) and those with higher income categories. Nearly 45% of these respondents (n = 147) reported often or always using their thermometer to check cooking doneness. The frequency of engaging in this behavior was best determined by four psychosocial constructs: behavioral intentions, beliefs about consequences, self-efficacy, and habits. Nearly two-thirds of respondents (64%; n = 333) reported often or always washing their poultry before cooking it. This behavior was more frequently reported by males (OR = 1.51, 95% CI = 1.002, 2.28). It was also predicted by six psychosocial constructs: behavioral intentions, beliefs about consequences, self-efficacy, social influences, social responsibility, and habits. Habits had the largest influence on both behaviors. The study results can inform the development of more targeted food safety education and outreach initiatives to improve these behaviors among Canadians.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".