Values and Preferences Related to Cancer Risk among Red and Processed Meat Eaters: A Pilot Cross-Sectional Study with Semi-Structured Interviews
Bibliographic record
Abstract
Introduction: Over the last decade, the possible impact of meat intake on overall cancer incidence and mortality has received considerable attention, and authorities have recommended decreasing consumption; however, the benefits of reducing meat consumption are small and uncertain. As such, individual decisions to reduce consumption are value- and preference-sensitive. Consequently, we undertook a pilot cross-sectional study to explore people’s values and preferences towards meat consumption in the face of cancer risk. Methods and analysis: The mixed-method pilot study included a quantitative questionnaire followed by qualitative evaluation to explore the dietary habits of 32 meat eaters, their reasons for eating meat, and willingness to change their meat consumption when faced with a potential risk reduction of cancer over a lifetime based on a systematic review and dose–response meta-analysis. We recruited a convenience sample of participants from two Canadian provinces: Nova Scotia and Prince Edward Island. This project was approved by the Research Ethics Board for Health Sciences research at Dalhousie University, Canada. Results: The average weekly consumption of red meat was 3.4 servings and the average weekly consumption of processed meat was 3 servings. The determinants that influenced meat intake were similar for both red and processed meat. Taste, cost, and family preferences were the three most commonly cited factors impacting red meat intake. Taste, cost, and (lack of) cooking time were the three most commonly cited factors impacting processed meat intake. None of the participants were willing to eliminate red or processed meat from their diet. About half of participants were willing to potentially reduce their meat consumption, with one third definitely willing to reduce their consumption. Strengths and limitations: This study is the first that we are aware of to share data with participants on the association of red meat and processed meat consumption and the risk of cancer mortality and cancer incidence, including the certainty of evidence for the risk reduction. The limitations of this study include its small sample size and its limited geographic sampling. Conclusions: When presented explicit information about the small uncertain cancer risk associated with red and processed meat consumption, study participants were unwilling to eliminate meat, while about one-third were willing to reduce their meat intake.
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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".