Diet-related awareness and behaviours in cancer survivors compared with non-cancer individuals: a pooled analysis of the HINTS study
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate diet-related cancer risk awareness and behaviours among cancer survivors compared with non-cancer individuals. DESIGN: It is a cross-sectional study initiated from the Health Information National Trends Survey (HINT). SETTING: Relevant survey questions from different iterations of HINTS were harmonised. Chi-square test and logistic regression models were performed to identify differences in diet-related cancer risk awareness and behaviours between the two groups. PARTICIPANTS: Participants in the study were drawn from the HINT survey with various variables including age, gender, race/ethnicity, education, marital status and BMI. RESULTS: The analysis revealed no significant differences in diet-related cancer risk awareness or behaviours between cancer survivors and non-cancer individuals. Those dietary factors included red and processed meat, alcohol, fibre, sugar-sweetened beverages, fruits and vegetables. Specifically, 82 % of both survivors and non-survivors failed to meet the American Cancer Society (ACS) recommendations for daily fruit consumption (OR = 0·91; 95 % CI = 0·77, 1·06), and approximately 75 % did not meet the daily vegetable intake guidelines (OR = 0·96; 95 % CI = 0·83, 1·11). The findings suggest that a cancer diagnosis does not inherently lead to improved dietary awareness or healthier eating behaviours. CONCLUSION: The lack of improvement in diet-related cancer risk awareness and behaviours among cancer survivors indicated missed education opportunities. The 'teachable moment' of cancer diagnosis was not effectively utilised, which highlighted a need for stronger guidance from healthcare providers. This gap may also reflect barriers, including limited training, time constraints and limited interprofessional collaboration among health professionals in delivering targeted dietary advice.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".