Factors influencing nutrition literacy in patients of colorectal cancer: a cross-sectional study
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) patients often experience varying degrees of malnutrition both pre-and post-treatment, highlighting the importance of their nutritional knowledge. However, studies on nutrition literacy (NL) in this population remain scarce. This study aims to evaluate the level of NL in colorectal cancer patients and identify key factors influencing NL. Methods: A total of 245 colorectal cancer patients participated in this study. The questionnaire included five sections: sociodemographic information, the Chinese Version of the Nutrition Literacy Assessment Instrument (CHI-NLit), the Montreal Cognitive Assessment Scale (MoCA), and the Hospital Anxiety and Depression Scale (HADS). Both univariate and multivariate analyses were performed to examine sociodemographic determinants of NL. We used Pearson and Spearman correlation coefficients to assess relationships between NL, MoCA and HADS. Results: The overall NL level among CRC patients was moderately low, with an average score of 19.224 ± 4.391-significantly below the normative neutrophil score of 21.5. Among the assessed dimensions, food groups received the lowest scores while food label calculation achieved the highest. Significant predictors influencing NL levels included age, years of education, family annual income, in review duration of illness, number of hospitalizations, memory and attention abilities and anxiety and depress symptoms. Conclusion: This study provides a comprehensive examination of NL in CRC patients. The findings indicate a relatively low level of NL within this group. Younger age, higher income levels, and urban residency correlated positively with elevated NL. Factors such as illness duration, number of hospitalizations, cognitive function measured by relevant scales are also emerged as significant determinants impacting NL. To enrich the research on NL, it is essential to conduct further data collection. From a clinical perspective, this evidence-based framework enables the development of stratified nutritional intervention protocols, specifically targeting vulnerable subgroups (e.g., elderly patients, rural dwellers, and those with extended illness duration). Such precision approaches hold significant potential to optimize dietary adherence, mitigate treatment-related complications, and ultimately enhance long-term quality of life in cancer survivorship care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".