Factors Associated With Limited Cancer Health Literacy Among Chinese People: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Limited cancer health literacy may be attributed to various factors. Although these factors play decisive roles in identifying individuals with limited cancer health literacy, they have not been sufficiently investigated, especially in China. There is a pressing need to ascertain the factors that effectively identify Chinese people with poor cancer health literacy. OBJECTIVE: This study aimed to identify the factor associated with limited cancer health literacy among Chinese people based on the 6-Item Cancer Health Literacy Test (CHLT-6). METHODS: We first categorized Chinese study participants according to the answers provided for cancer health literacy as follows: people who provided ≤3 correct answers were labeled as having limited cancer health literacy, whereas those who provided between 4 and 6 correct answers were labeled as having adequate cancer health literacy. We then adopted logistic regression to analyze the factors that were closely related to limited cancer health literacy among at-risk study participants. RESULTS: The logistic regression analysis identified the following factors that effectively predicted limited cancer health literacy: (1) male gender, (2) low education attainment, (3) age, (4) high levels of self-assessed general disease knowledge, (5) low levels of digital health literacy, (6) limited communicative health literacy, (7) low general health numeracy, and (8) high levels of mistrust in health authorities. CONCLUSIONS: Using regression analysis, we successfully identified 8 factors that could be used as predictors of limited cancer health literacy among Chinese populations. These findings have important clinical implications for supporting Chinese people with limited cancer health literacy through the development of more targeted health educational programs and resources that better align with their actual skill levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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".