Nutrition and Cancer Prevention: Why is the Evidence Lost in Translation?
Bibliographic record
Abstract
With the high burden of cancer worldwide, primary prevention has been identified as a key cancer control strategy to reduce this burden. Diet and nutrition are important modifiable factors that may alter the risk of developing cancer, because several dietary components including alcohol consumption, fruit and vegetable intake, and dietary fiber have been shown to significantly impact cancer risk. Consequently, a number of organizations have developed cancer prevention guidelines that highlight the importance of nutrition (and related factors including body size and physical activity) to reduce the risk of cancer. However, there are barriers to the uptake of these guidelines, particularly with respect to diet and nutrition including awareness, communication, and other factors that influence eating behavior. Improved knowledge translation (KT) of recommendations may help facilitate uptake. The purposes of this narrative review are: 1) to examine issues and challenges related to KT of diet and nutrition evidence in the context of cancer prevention, including public awareness and attitudes towards cancer prevention, engagement in cancer prevention strategies, and effects of KT on diet-cancer preventive behaviors; 2) to discuss examples of effective and ineffective KT of diet and nutrition evidence; and 3) to provide recommendations for improving KT to help move the field of diet, nutrition, and cancer prevention forward. Evidence shows that adherence to nutrition recommendations for cancer prevention significantly reduces the risk of cancer; however, engagement in nutrition-based preventative behaviors is low. Skepticism and confusion around evidence linking diet and nutrition with cancer may arise, in part, through ineffective media KT; the primary source of health information for many people. Simple, tailored, targeted KT communication strategies aimed at increasing the general public's awareness, attitudes, and engagement in cancer preventive behavior should be emphasized to encourage cancer control.
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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 | 0.124 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".