Assessing cancer prevention literacy among European population: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Cancer prevention literacy (CPL) is essential for empowering individuals to make health-informed decisions and adopt preventive measures to reduce their cancer risk. This study aims to measure CPL among the European population. Methods A cross-sectional population-based study was conducted from February to March 2024 among European residents over 18 years old (N = 2312) in the context of BUMPER Project (https://bumper.cancer.eu/). An online self-administered questionnaire was developed including: sociodemographic characteristics (age, gender, country of residence, educational level), and CPL (general and by European Code Against Cancer -ECAC- topics). A descriptive analysis was performed and the chi-square and Kruskal-Wallis test were calculated to examine the relationship between CPL and sociodemographic variables. Results 44.2% showed a high level of CPL, followed by 39.5% with medium, and 16.3% with low level. 61.1% of participants are not aware of ECAC. ECAC topics with higher levels of knowledge are: UV exposure (88.3%), pollution (81.6%), tobacco (78.7%), second-hand smoking (60.9%). ECAC topics with lower levels are: hormone replacement therapy (47.9%), breastfeeding (41.6%), and vaccination (32.9%). Statistically significant differences (p < 0.05) are observed in CPL level by gender, country of residence, and educational level. Those with a higher % of low CPL levels are men (22.8%), people from West and South Europe (20.8% and 18.1%), and primary education level (34.7%). Conclusions CPL level is medium-high among the European population, with social and gender inequalities. Further studies are needed to delve into these inequalities. Key messages • The cancer prevention literacy level is medium-high among the European population, with social and gender inequalities. • There is a need of tailored interventions to address social inequalities in cancer prevention literacy among European population.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".