Effectiveness of mechanically tenderized beef labels on influencing practices of cooking beef in British Columbia
Bibliographic record
Abstract

 Background: Mechanically tenderized beef poses a higher risk for Escherichia coli 0157:H7 infection than intact beef and has been implicated in several outbreaks. As such, all products are mandated to be labeled in Canada. Purpose: This study assessed the effectiveness of mechanically tenderized beef labels on influencing practices of cooking beef in British Columbia. Methods: 74 adults within British Columbia who cooked beef were surveyed electronically using a snowball method. An inferential (Pearson chi-square analysis) and descriptive analysis was performed on the nominal data in PSPP and Microsoft Excel respectively. Results: Only 8% of respondents abided with information on mechanically tenderized beef labels. No statistically significant associations were found between practices of abiding with information on mechanically tenderized beef labels and various socio-demographic factors (e.g. age, gender, education level, and food safety education) (p<0.01). The practice of not using food thermometers was the major contributing factor that lowered the effectiveness of mechanically tenderized beef labels. Conclusion: Mechanically tenderized beef labels were ineffective in influencing behaviours of cooking beef in British Columbia. Therefore, other risk communication strategies are needed to persuade adults in British Columbia to adequately cook mechanically tenderized beef products. Recommendations: Future studies can assess whether the general public is properly cooling mechanically tenderized beef as the label does not address this practice.
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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 | 0.004 | 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".