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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".