Knowledge and Beliefs of Cancer Risk Factors and Early Cancer Symptoms in Lebanon: A Cross-sectional Survey Among Adults in the Community
Bibliographic record
Abstract
Background Lebanon has an increasing cancer burden. Sufficient knowledge of cancer risk factors and early cancer symptoms can help lower cancer burden by facilitating primary prevention and early diagnosis. This study (i) assessed Lebanese adults’ knowledge and beliefs of cancer risk factors and early cancer symptoms, (ii) analyzed whether knowledge was correlated with personal behavior, and (iii) assessed the presence of barriers that keep knowledge from turning into healthcare seeking behavior. Methods We performed a cross-sectional survey in the Lebanese adult population, consisting of a questionnaire administered during face-to-face interviews on a community-based non-probability sample (n = 726) that was frequency matched to national government estimates on age, level of education and gender. Results Recognition was high for carcinogens and protective factors (75%), but low for neutral factors (22%) which were often seen as carcinogenic. A quarter of participants (27.8%) could not name any early warning signs. For some risk factors, high knowledge scores were correlated with low-risk behavior, but this was not the case for cigarette smoking. The most frequent barriers for not seeking timely care were financial (57.0%) fear of finding illness (53.7%), and having other things to worry about (42.4%). Conclusion This study revealed important knowledge gaps which are likely to hamper primary prevention and early diagnosis. However, we also showed that high knowledge of risk was not always correlated with low-risk behavior. This, together with the barriers we found that kept people from seeking timely health care, emphasizes that efforts to lower cancer burden should not only focus on increasing knowledge.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".