Geographic, Demographic, and Socioeconomic Disparities and Factors Associated With Cancer Literacy in China: National Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Cancer literacy is associated with several health-related behaviors and outcomes. However, there is still a lack of nationwide surveys for cancer literacy in China. OBJECTIVE: This study aims to evaluate cancer literacy in China, explore disparities, and provide scientific evidence for policy makers. METHODS: A cross-sectional survey was conducted in mainland China in 2021 using the multistage probability proportional to the size sampling method. Both the reliability and validity of the questionnaire were evaluated. The awareness levels were adjusted by sampling weights and nonrepresentativeness weights to match the actual population distributions. The Rao-Scott adjusted chi-square test was applied to test geographic, demographic, and socioeconomic disparities. A generalized linear model was used to explore potential factors. RESULTS: A total of 80,281 participants aged 15-74 years were finally enrolled from 21 provinces, with an overall response rate of 89.32%. The national rate of cancer literacy was 70.05% (95% CI 69.52%-70.58%). The rates were highest regarding knowledge of cancer management (74.96%, 95% CI 74.36%-75.56%) but were lowest regarding basic knowledge of cancer (66.77%, 95% CI 66.22%-67.33%). Cancer literacy was highest in East China (72.65%, 95% CI 71.82%-73.49%), Central China (71.73%, 95% CI 70.65%-72.81%), and North China (70.73%, 95% CI 68.68%-72.78%), followed by Northeast (65.38%, 95% CI 64.54%-66.22%) and South China (63.21%, 95% CI 61.84%-64.58%), whereas Southwest (59.00%, 95% CI 58.11%-59.89%) and Northwest China (57.09%, 95% CI 55.79%-58.38%) showed a need for improvement. Demographic and socioeconomic disparities were also observed. Urban dwellers, the Han ethnic group, and population with higher education level or household income were associated with prior knowledge. The questionnaire showed generally good internal and external reliability and validity. CONCLUSIONS: It remains important for China to regularly monitor levels of cancer literacy, narrow disparities, and strengthen health education for dimensions with poor performance and for individuals with limited knowledge to move closer to the goal of Healthy China 2030.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".